Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Background
Calculating Best Fits to the Image Data
Methods
Results
Discussion
Conclusions and Future Work
References

Using Blur to Affect Perceived Distance and Size

Profile image of James O'BrienJames O'Brien
https://doi.org/10.1145/1731047.1731057
visibility

…

description

16 pages

link

1 file

Abstract

We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image's contents. From the model, we develop a semi-automated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene's contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model's predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently.

Figures (15)
Fig. 1. (a) Rendering a cityscape with a pinhole aperture results in no perceptible blur. The scene looks large and far away. (b) Simulating a 60m- wide aperture produces blur consistent with a shallow depth of field, making the scene appear to be a miniature model. Original city images and data from GoogleEarth are copyright Terrametrics, SanBorn, and Google.
Fig. 1. (a) Rendering a cityscape with a pinhole aperture results in no perceptible blur. The scene looks large and far away. (b) Simulating a 60m- wide aperture produces blur consistent with a shallow depth of field, making the scene appear to be a miniature model. Original city images and data from GoogleEarth are copyright Terrametrics, SanBorn, and Google.
Fig. 4. The Scheimpflug Principle. Tilt-and-shift lenses cause the orienta- tion of the focal plane to shift and rotate relative to the image plane. As a result, the apparent depth of field in an image can be drastically changed and the photographer has greater control over which objects are in focus and which are blurred.
Fig. 4. The Scheimpflug Principle. Tilt-and-shift lenses cause the orienta- tion of the focal plane to shift and rotate relative to the image plane. As a result, the apparent depth of field in an image can be drastically changed and the photographer has greater control over which objects are in focus and which are blurred.
Fig. 5. Comparison of blur patterns produced by three rendering techniques: consistent blur (a), simulated tilt-and-shift lens (b), and linear blur gradient (c) The settings in (b) and (c) were chosen to equate the maximum blur-circle diameters with those in (a). The percent differences in blur-circle diameters betweer the images are plotted in (d), (e), and (f). Panels (d) and (e) show that the simulated tilt-and-shift lens and linear blur gradient do not closely approximate consistent blur rendering. The large differences are due to the buildings, which protrude from the ground plane. Panel (f) shows that the linear blur gradient provides essentially the same blur pattern as a simulated tilt-and-shift lens. Most of the differences in (f) are less than 7%; the only exceptions are in the banc near the center, where the blur diameters are less than one pixel and not detectable in the final images.
Fig. 5. Comparison of blur patterns produced by three rendering techniques: consistent blur (a), simulated tilt-and-shift lens (b), and linear blur gradient (c) The settings in (b) and (c) were chosen to equate the maximum blur-circle diameters with those in (a). The percent differences in blur-circle diameters betweer the images are plotted in (d), (e), and (f). Panels (d) and (e) show that the simulated tilt-and-shift lens and linear blur gradient do not closely approximate consistent blur rendering. The large differences are due to the buildings, which protrude from the ground plane. Panel (f) shows that the linear blur gradient provides essentially the same blur pattern as a simulated tilt-and-shift lens. Most of the differences in (f) are less than 7%; the only exceptions are in the banc near the center, where the blur diameters are less than one pixel and not detectable in the final images.
Here we see that we can achieve the same amount of blur as en- countered with a focal distance of 2) = m2zo by shooting the scene at distance z and setting the diameter of the camera aperture to A= A/m. The aperture must therefore be quite small to make a scene look much larger than it is and this limits the amount of available light, causing problems with signal-to-noise ratio, motion, and so forth. Likewise the aperture must be quite large to make the scene look much smaller than it actually is, and such large aper- tures might be difficult to achieve with a physical camera. Because of these limitations, it is quite attractive to be able to use a con-
Here we see that we can achieve the same amount of blur as en- countered with a focal distance of 2) = m2zo by shooting the scene at distance z and setting the diameter of the camera aperture to A= A/m. The aperture must therefore be quite small to make a scene look much larger than it is and this limits the amount of available light, causing problems with signal-to-noise ratio, motion, and so forth. Likewise the aperture must be quite large to make the scene look much smaller than it actually is, and such large aper- tures might be difficult to achieve with a physical camera. Because of these limitations, it is quite attractive to be able to use a con-
Fig. 6. Focal distance as a function of relative distance and retinal-image blur. Relative distance is defined as the ratio of the distance to an object and the distance to the focal plane. The three colored curves represent different amounts of image blur expressed as the diameter of the blur circle, c, in degrees. We use angular units because in those units, the image device’s fo- cal length drops out [Kingslake 1992]. The variance in the distribution was determined by assuming that pupil diameter is Gaussian distributed with a mean of 4.6mm and standard deviation of 1mm [Spring and Stiles 1948]. For a given amount of blur, it is impossible to recover the original focal distance without knowing the relative distance. Note that as the relative dis- tance approaches 1, the object moves closer to the focal plane. There is a singularity at a relative distance of 1 because the object is by definition completely in focus at that distance.
Fig. 6. Focal distance as a function of relative distance and retinal-image blur. Relative distance is defined as the ratio of the distance to an object and the distance to the focal plane. The three colored curves represent different amounts of image blur expressed as the diameter of the blur circle, c, in degrees. We use angular units because in those units, the image device’s fo- cal length drops out [Kingslake 1992]. The variance in the distribution was determined by assuming that pupil diameter is Gaussian distributed with a mean of 4.6mm and standard deviation of 1mm [Spring and Stiles 1948]. For a given amount of blur, it is impossible to recover the original focal distance without knowing the relative distance. Note that as the relative dis- tance approaches 1, the object moves closer to the focal plane. There is a singularity at a relative distance of 1 because the object is by definition completely in focus at that distance.
Fig. 7. Bayesian analysis of blur as cue to absolute distance. (a) The probability distribution P(z,,d|c) where c is the observed blur diameter in the image (in this case, 0.1°), zo is the focal distance, and d is the relative distance of another point in the scene. Measuring the blur produced by an object cannot reveal the absolute or relative distance to points in the scene. (b) The probability distribution P(zo,d|p) where p is the observed perspective. Perspective specifies the relative distance, but not the absolute distance: it is scale ambiguous. (c) The product of the distributions in (a) and (b). From this posterior distribution, the absolute and relative distances of points in the scene can be estimated.
Fig. 7. Bayesian analysis of blur as cue to absolute distance. (a) The probability distribution P(z,,d|c) where c is the observed blur diameter in the image (in this case, 0.1°), zo is the focal distance, and d is the relative distance of another point in the scene. Measuring the blur produced by an object cannot reveal the absolute or relative distance to points in the scene. (b) The probability distribution P(zo,d|p) where p is the observed perspective. Perspective specifies the relative distance, but not the absolute distance: it is scale ambiguous. (c) The product of the distributions in (a) and (b). From this posterior distribution, the absolute and relative distances of points in the scene can be estimated.
Fig. 8. The four types of blur used in the analysis and experiment: (a) no blur, (b-c) consistent blur, (d-e) linear vertical blur gradient, and (f-g) linear horizontal blur gradient. Simulated focal distances of 0.15m (b,d,f) and 0.06m (c,e,g) are shown. In approximating the blur produced by a short focal length, the consistent-blur condition produces the most accurate blur, followed by the vertical gradient, the horizontal gradient, and the no-blur condition. Original city images and data from GoogleEarth are copyright Terrametrics, SanBorn, and Google.
Fig. 8. The four types of blur used in the analysis and experiment: (a) no blur, (b-c) consistent blur, (d-e) linear vertical blur gradient, and (f-g) linear horizontal blur gradient. Simulated focal distances of 0.15m (b,d,f) and 0.06m (c,e,g) are shown. In approximating the blur produced by a short focal length, the consistent-blur condition produces the most accurate blur, followed by the vertical gradient, the horizontal gradient, and the no-blur condition. Original city images and data from GoogleEarth are copyright Terrametrics, SanBorn, and Google.
Figure 11(a), we use the technique in Algorithm 1, originally de- scribed by Okatani and DeGuchi [2007]. If the scene is uncarpen- tered, like the landscape in Figure 11(b), then a grid is displayed over the image. The viewer uses a mouse to rotate the grid until it appears to be parallel to the scene. The orientation of the grid yields o and 7. Parameters | and v (defined in Figure 10) are re- covered from the settings (in our case, from OpenGL) that were used to render the grid on-screen. Finally, Algorithm 2 determines the amount of blur assigned to each pixel, then creates the final image.
Figure 11(a), we use the technique in Algorithm 1, originally de- scribed by Okatani and DeGuchi [2007]. If the scene is uncarpen- tered, like the landscape in Figure 11(b), then a grid is displayed over the image. The viewer uses a mouse to rotate the grid until it appears to be parallel to the scene. The orientation of the grid yields o and 7. Parameters | and v (defined in Figure 10) are re- covered from the settings (in our case, from OpenGL) that were used to render the grid on-screen. Finally, Algorithm 2 determines the amount of blur assigned to each pixel, then creates the final image.
Fig. 10. Schematic of variables pertinent to the semi-automated blurring algorithm. Here, the image surface is equivalent to the monitor surface, and v and 1 are in units of pixels. o indicates the angle between the ground plane’s surface normal and the imaging system’s optical axis. Refer to Al- gorithm | for details on how each value can be calculated from an input image. (Adapted from Okatani and Deguchi [2007]) Fig. 9. Determining the most likely focal distance from blur and perspective. Intended focal distance was 0.06m. Each panel plots estimated focal distance as a function of relative distance. The left, middle, and right panels show the estimates for consistent blur, vertical blur gradient, and horizontal blur gradient, respectively. The first step in the analysis is to extract the relative-distance and blur information from several points in the image. The values for each point are then used with Eq. (2) to estimate the focal distance. Each estimate is represented by a point. Then all of the focal distance estimates are accumulated to form a marginal distribution of estimates (shown on the right of each panel). The data from a consistent-blur rendering most closely matches the selected curve, resulting in extremely low variance. Though the vertical blur gradient incorrectly blurs several pixels, it is well correlated with the relative distances in the scene, so it too produces a marginal distribution with low variance. The blur applied by the horizontal gradient is mostly uncorrelated with relative distance, resulting in a marginal distribution with large variance and therefore the least reliable estimate.
Fig. 10. Schematic of variables pertinent to the semi-automated blurring algorithm. Here, the image surface is equivalent to the monitor surface, and v and 1 are in units of pixels. o indicates the angle between the ground plane’s surface normal and the imaging system’s optical axis. Refer to Al- gorithm | for details on how each value can be calculated from an input image. (Adapted from Okatani and Deguchi [2007]) Fig. 9. Determining the most likely focal distance from blur and perspective. Intended focal distance was 0.06m. Each panel plots estimated focal distance as a function of relative distance. The left, middle, and right panels show the estimates for consistent blur, vertical blur gradient, and horizontal blur gradient, respectively. The first step in the analysis is to extract the relative-distance and blur information from several points in the image. The values for each point are then used with Eq. (2) to estimate the focal distance. Each estimate is represented by a point. Then all of the focal distance estimates are accumulated to form a marginal distribution of estimates (shown on the right of each panel). The data from a consistent-blur rendering most closely matches the selected curve, resulting in extremely low variance. Though the vertical blur gradient incorrectly blurs several pixels, it is well correlated with the relative distances in the scene, so it too produces a marginal distribution with low variance. The blur applied by the horizontal gradient is mostly uncorrelated with relative distance, resulting in a marginal distribution with large variance and therefore the least reliable estimate.
Fig. 11. Input and output of the semiautomated blurring algorithm. The algorithm can estimate the blur pattern required to simulate a desired focal length. | can either derive scene information from parallel lines in a scene or use manual feedback from the user on the overall orientation of the scene. (a) Two pairs o parallel lines were selected from a carpentered scene for use with the first approach. (b) The resulting image once blur was applied. Intended focal distance se to 0.06m. (c) A grid was manually aligned to lie parallel to the overall scene. (d) The blurred output designed to simulate a focal distance of 0.50m.
Fig. 11. Input and output of the semiautomated blurring algorithm. The algorithm can estimate the blur pattern required to simulate a desired focal length. | can either derive scene information from parallel lines in a scene or use manual feedback from the user on the overall orientation of the scene. (a) Two pairs o parallel lines were selected from a carpentered scene for use with the first approach. (b) The resulting image once blur was applied. Intended focal distance se to 0.06m. (c) A grid was manually aligned to lie parallel to the overall scene. (d) The blurred output designed to simulate a focal distance of 0.50m.
Fig. 12. Results of the psychophysical experiment averaged across the seven subjects. Panels (a) and (b) respectively show the data when the images had low and high depth variation. The type of blur manipulation is indicated by the colors and shapes of the data points. Blue squares for consistent blur, green circles for vertical blur gradient, and red triangles for horizontal blur gradient. Error bars represent standard errors. Individual subject data are included in the supplemental material.
Fig. 12. Results of the psychophysical experiment averaged across the seven subjects. Panels (a) and (b) respectively show the data when the images had low and high depth variation. The type of blur manipulation is indicated by the colors and shapes of the data points. Blue squares for consistent blur, green circles for vertical blur gradient, and red triangles for horizontal blur gradient. Error bars represent standard errors. Individual subject data are included in the supplemental material.

Related papers

Blur in Human Vision and Increased Visual Realism in Virtual Environments
Sevil Sariyildiz

Lecture Notes in Computer Science

A challenge for virtual reality (VR) applications is to increase the realism of an observer's visual experience. For this purpose the variation of the blur that an observer experiences in his/her vision, while he/she focuses on a particular location, can be mimicked by blurring the VR computer graphics based on a model of the blur. The blur in human vision is the result of a combination of optical and neural vision processes; namely optical refraction, nonuniform retinal sampling, and cortical magnification. Due to the complexity of the phenomenon, apparently no theoretical model of the blur has been published. In this work we model the combined effect by means of a probabilistic model of the human visual system. The results from the models match common vision experience verifying the validity of the underlying theoretical considerations. The implementation of the model for increased realism in virtual reality is illustrated by means of a rendering of a virtual reality scene, which is processed for two different acts of focusing.

View PDFchevron_right
A no-reference perceptual blur metric
Touradj Ebrahimi

2002

Abstract We present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimization of source coding, network resource management and autofocus of an image capturing device.

View PDFchevron_right
On measuring the local perceived blur in natural scenes both in the lab and online
Peter Bex

2018

Natural retinal images contain blur. Image blur varies across the visual field depending on the three dimensional image structure and the observers’ point of accommodation. There is currently no consensus in the field on how to quantify local perceived blur in natural images. We developed a biologically-plausible measure of local image blur, local slope, which is computed from the output of a set spatial frequency and orientation band-limited filters. To determine if local slope captured the perception of image blur variation, we used a task in which observers traced regions in natural images that matched the perceived blur of a standard blur. We ran our tracing task both under controlled laboratory conditionswith a small number of observers and online using a large sample of Amazon Mechanical Turk Workers. There was good agreement across observes on image regions with matched perceived blur and a correlation between local slopes of blur-matching areas of natural and standard images...

View PDFchevron_right
Judging an unfamiliar object's distance from its retinal image size
Jeroen Smeets

Journal of vision, 2011

How do we know how far an object is? If an object's size is known, its retinal image size can be used to judge its distance. To some extent, the retinal image size of an unfamiliar object can also be used to judge its distance, because some object sizes are more likely than others. To examine whether assumptions about object size are used to judge distance, we had subjects indicate the distance of virtual cubes in complete darkness. In separate sessions, the simulated cube size either varied slightly or considerably across presentations. Most subjects indicated a further distance when the simulated cube was smaller, showing that they used retinal image size to judge distance. The cube size that was considered to be most likely depended on the simulated cubes on previous trials. Moreover, subjects relied twice as strongly on retinal image size when the range of simulated cube sizes was small. We conclude that the variability in the perceived cube sizes on previous trials influences the range of sizes that are considered to be likely.

View PDFchevron_right
Occlusion edge blur: a cue to relative visual depth
Jannick Rolland

Journal of the Optical Society of America A, 1996

We studied whether the blur/sharpness of an occlusion boundary between a sharply focused surface and a blurred surface is used as a relative depth cue. Observers judged relative depth in pairs of images that di ered only in the blurriness of the common boundary between two adjoining texture regions, one blurred and one sharply focused. Two experiments were conducted; in both, observers consistently used the blur of the boundary as a cue to relative depth. However, the strength of the cue, relative to other cues, varied across observers. The occlusion edge blur cue can resolve the near/far ambiguity inherent in depth-from-focus computations.

View PDFchevron_right
Depth of Focus and Perceived Blurring of Simultaneously-Viewed Displays
Robert Patterson

This paper is published in the interest of scientific and technical information exchange and its publication does not constitute the Government's approval or disapproval of its idea or findings.

View PDFchevron_right
Image blurring effects due to depth discontinuities: blurring that creates emergent image details
Thắng Nguyễn

Image and Vision Computing, 1992

A new model (called multi-component blurring-or MCB) to account for image blurring effects due to depth discontinuties is presented. We show that blurring processes operating in the vicinity of large depth discontinuties can give rise to emergent image details, quite distinguishable but nevertheless unexplained by previously available blurring models. In other words, the maximum principle for scale space' does not hold. It is argued that blurring in high-relief 30 scenes should be more accurately modelled as a multi-component process. We present results from extensive and carefully designed rxperiments, with many images of real scenes taken by a CCD camera with typical parameters. These results have consistently supported our new blurring model. Due care was taken to ensure that the image phenomena observed are mainly due to de-focussing and not due to mutual illuminations?, specularity", objects' 'finer' .structures, coherent diffraction, or incidental image noises'. We also hypothesize on the role of blurring on human depth-from-blur perception, based on correlation with recent results from human blur perception'.

View PDFchevron_right
An efficient no-reference metric for perceived blur
Judith Redi

3rd European Workshop on Visual Information Processing, 2011

This paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background 1 . Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability.

View PDFchevron_right
Blur and Disparity Are Complementary Cues to Depth
emily cooper

Current Biology, 2012

Estimating depth from binocular disparity is extremely precise and the cue does not depend on statistical regularities in the environment. Thus, disparity is commonly regarded as the best visual cue for determining 3D layout. But depth from disparity is only precise near where one is looking; it is quite imprecise elsewhere [1]. To overcome this imprecision away from fixation, vision resorts to using other depth cues-e.g., linear perspective, familiar size, aerial perspective. But those cues depend on statistical regularities in the environment and are therefore not always reliable [5]. Depth from defocus blur relies on fewer assumptions and has the same geometric constraints as disparity [6], but different physiological constraints . Hence, blur could in principle fill in the parts of visual space where disparity is imprecise . We tested this possibility with a depth-discrimination experiment. We found that disparity was more precise near fixation and that blur was indeed more precise away from fixation. When both cues were available, observers relied on the more informative one. Blur appears to play an important, previously unrecognized role in depth perception. Our findings lead to a new hypothesis about the evolution of slit-shaped pupils and have noteworthy implications for the design and implementation of stereo 3D viewing systems.

View PDFchevron_right
Depth-dependent blur adaptation
Peter Battaglia

2004

Variations in blur are present in retinal images of scenes containing objects at multiple depth planes. Here we examine whether neural representations of image blur can be recalibrated as a function of depth. Participants were exposed to textured images whose blur changed with depth in a novel manner. For one group of participants, image blur increased as the images moved closer; for the other group, blur increased as the images moved away.

View PDFchevron_right

References (60)

  1. AKELEY, K., WATT, S. J., GIRSHICK, A. R., AND BANKS, M. S. 2004. A stereo display prototype with multiple focal distances. ACM Trans. Graph. 23, 3, 804-813.
  2. BARSKY, B. A. 2004. Vision-realistic rendering: simulation of the scanned foveal image from wavefront data of human subjects. In APGV '04: Pro- ceedings of the 1st Symposium on Applied perception in graphics and visualization. 73-81.
  3. BARSKY, B. A., HORN, D. R., KLEIN, S. A., PANG, J. A., AND YU, M. 2003a. Camera models and optical systems used in computer graph- ics: Part I, Object-based techniques. In In Proceedings of the 2003 International Conference on Computational Science and its Applica- tions (ICCSA'03), Montreal, Second International Workshop on Com- puter Graphics and Geometric Modeling (CGGM'2003). 246-255.
  4. BARSKY, B. A., HORN, D. R., KLEIN, S. A., PANG, J. A., AND YU, M. 2003b. Camera models and optical systems used in computer graph- ics: Part II, image-based techniques. In In Proceedings of the 2003 International Conference on Computational Science and its Applica- tions (ICCSA'03), Montreal, Second International Workshop on Com- puter Graphics and Geometric Modeling (CGGM'2003). 256-265.
  5. BELL, J. A. 1924. Theory of mechanical miniatures in cinematography. Transactions of the SMPTE 18, 119.
  6. BRILLAULT-O'MAHONY, B. 1991. New method for vanishing-point de- tection. CVGIP: Image Underst. 54, 2, 289-300.
  7. BURGE, J. D., FOWLKES, C. C., , AND BANKS, M. 2010. Natural-scene statistics predict how the figure-ground cue of convexity affects human depth perception. Journal of Neuroscience. In press.
  8. CANNY, J. 1986. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on PAMI-8, 6 (Nov.), 679-698.
  9. CHINNOCK, C. 2009. Personal Communication.
  10. COLE, F., DECARLO, D., FINKELSTEIN, A., KIN, K., MORLEY, K., AND SANTELLA, A. 2006. Directing gaze in 3d models with stylized focus. Eurographics Symposium on Rendering, 377-387.
  11. COOK, R. L., CARPENTER, L., AND CATMULL, E. 1987. The REYES image rendering architecture. SIGGRAPH Comput. Graph. 21, 4, 95- 102.
  12. COOK, R. L., PORTER, T., AND CARPENTER, L. 1984. Distributed ray tracing. SIGGRAPH Comput. Graph. 18, 3, 137-145.
  13. COUGHLAN, J. M. AND YUILLE, A. L. 2003. Manhattan world: Ori- entation and outlier detection by bayesian inference. Neural Computa- tion 15, 5, 1063-1088.
  14. DIPAOLA, S., RIEBE, C., AND ENNS, J. 2009. Rembrandt's textural agency: A shared perspective in visual art and science. Leonardo In Press.
  15. EGUSA, H. 1983. Effects of brightness, hue, and saturation on perceived depth between adjacent regions in the visual field. Perception 12, 167- 175. FEARING, P. 1995. Importance ordering for real-time depth of field. In Proceedings of the Third International Computer Science Conference on Image Analysis Applications and Computer Graphics. 372-380.
  16. FIELDING, R. 1985. Special effects cinematography, Fourth ed. Focal Press, Oxford.
  17. FISHER, S. K. AND CIUFFREDA, K. J. 1988. Accommodation and appar- ent distance. Perception 17, 609-621.
  18. FLICKR. 2009. Flickr group: Tilt shift miniaturization fakes.
  19. FRY, G. A., BRIDGEMAN, C. S., AND ELLERBROCK, V. J. 1949. The ef- fects of atmospheric scattering on binocular depth perception. American Jounral of Optometry and Archives of American Academy of Optome- try 26, 9-15.
  20. GREEN, P., SUN, W., MATUSIK, W., AND DURAND, F. 2007. Multi- aperture photography. ACM Trans. Graph. 26, 3, 68.
  21. HAEBERLI, P. AND AKELEY, K. 1990. The accumulation buffer: hardware support for high-quality rendering. SIGGRAPH Comput. Graph. 24, 4, 309-318.
  22. HECHT, H., KAISER, M. K., AND BANKS, M. S. 1996. Gravitational acceleration as a cue for absolute size and distance? Perception & Psy- chophysics 58, 1066-1075.
  23. HILLAIRE, S., L ÉCUYER, A., COZOT, R., AND CASIEZ, G. 2007. Depth- of-field blur effects for first-person navigation in virtual environments. In VRST '07: Proceedings of the 2007 ACM symposium on Virtual reality software and technology. 203-206.
  24. HILLAIRE, S., LECUYER, A., COZOT, R., AND CASIEZ, G. 2008. Us- ing an eye-tracking system to improve camera motions and depth-of-field blur effects in virtual environments. Virtual Reality Conference, 2008. VR '08. IEEE, 47-50.
  25. ICHIHARA, S., KITAGAWA, N., AND AKUTSU, H. 2007. Contrast and depth perception: effects of texture contrast and area contrast. Percep- tion 36, 686-695.
  26. KINGSLAKE, R. 1992. Optics in photography. SPIE Optical Engineering Press, Bellingham, Wash.
  27. KOLB, C., MITCHELL, D., AND HANRAHAN, P. 1995. A realistic camera model for computer graphics. In Proceedings of ACM SIGGRAPH. 317- 324.
  28. KOSARA, R., MIKSCH, S., HAUSER, F., SCHRAMMEL, J., GILLER, V., AND TSCHELIGI, M. 2002. Useful properties of semantic depth of field for better f+c visualization. Joint Eurographics -IEEE TCVG Symposium on Visualization, 205-210.
  29. KOSARA, R., MIKSCH, S., AND HAUSER, H. 2001. Semantic depth of field. Proceedings of the IEEE Symposium on Information Visualization, 97-104.
  30. LAFORET, V. 2007. A really big show (May 31, 2007). New York Times.
  31. LARSEN, J. S. 1971. Sagittal growth of the eye. Acta Opthalmologica 49, 6, 873-886.
  32. LEVIN, A., FERGUS, R., DURAND, F., AND FREEMAN, W. T. 2007. Im- age and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26, 3, 70-1 -70-8.
  33. MARSHALL, J., BURBECK, C., ARIELY, D., ROLLAND, J., AND MARTIN, K. 1996. Occlusion edge blur: A cue to relative visual depth. Journal of the Optical Society of America A 13, 681-688. 19:16 • R. Held et al.
  34. MASAOKA, K., HANAZATO, A., EMOTO, M., YAMANOUE, H., NOJIRI, Y., AND OKANO, F. 2006. Spatial distortion prediction system for stereo- scopic images. Journal of Electronic Imaging 15, 1, 013002-1 -013002- 12.
  35. MATHER, G. 1996. Image blur as a pictorial depth cue. Proceedings of the Royal Society: Biological Sciences 263, 1367, 169-172.
  36. MATHER, G. AND SMITH, D. R. R. 2000. Depth cue integration: Stereop- sis and image blur. Vision Research 40, 25, 3501-3506.
  37. MATHER, G. AND SMITH, D. R. R. 2002. Blur discrimination and it's relationship to blur-mediated depth perception. Perception 31, 10, 1211- 1219.
  38. MCCLOSKEY, M. AND LANGER, M. 2009. Planar orientation from blur gradients in a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2318-2325.
  39. MEESTERS, L., IJSSELSTEIJN, W., AND SEUNTIENS, P. 2004. A survey of perceptual evaluations and requirements of three-dimensional tv. Circuits and Systems for Video Technology, IEEE Transactions on 14, 3 (March), 381-391.
  40. MON-WILLIAMS, M. AND TRESILIAN, J. R. 2000. Ordinal depth infor- mation from accommodation. Ergonomics 43, 3, 391-404.
  41. MORENO-NOGUER, F., BELHUMEUR, P. N., AND NAYAR, S. K. 2007. Active refocusing of images and videos. ACM Trans. Graph. 26, 3, 67-1 -67-9.
  42. MULDER, J. D. AND VAN LIERE, R. 2000. Fast perception-based depth of field rendering. In Proceedings of the ACM symposium on Virtual reality software and technology. 129-133.
  43. OKATANI, T. AND DEGUCHI, K. 2007. Estimating scale of a scene from a single image based on defocus blur and scene geometry. In Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on. 1-8.
  44. PALMER, S. E. AND BROOKS, J. L. 2008. Edge-region grouping in figure- ground organization and depth perception. Journal of Experimental Psy- chology: Human Perception and Performance. 24, 6 (12), 1353-1371.
  45. PENTLAND, A. P. 1987. A new sense for depth of field. IEEE Transactions on Pattern Analysis and Machine 9, 4, 523-531.
  46. POTMESIL, M. AND CHAKRAVARTY, I. 1981. A lens and aperture camera model for synthetic image generation. SIGGRAPH Comput. Graph. 15, 3, 297-305.
  47. ROHALY, A. AND WILSON, H. 1999. The effects of contrast on perceived depth and depth discriminations. Vision Research 39, 9-18.
  48. ROKITA, P. 1996. Generating depth of-field effects in virtual reality appli- cations. Computer Graphics and Applications, IEEE 16, 2 (Mar), 18-21.
  49. SAXBERG, B. V. H. 1987. Projected free fall trajectories: 1. theory and simulation. Biological Cybernetics 56, 159-175.
  50. SCHECHNER, Y. Y. AND KIRYATI, N. 2000. Depth from defocus vs. stereo: How different really are they? International Journal of Computer Vi- sion 29, 2, 141-162.
  51. SCHIFFMAN, B. 2008. Movie industry doubles down on 3D. Wired Maga- zine.
  52. SEDGWICK, H. A. 1986. Space Perception. Wiley.
  53. SPRING, K. AND STILES, W. S. 1948. Variation of pupil size with change in the angle at which the light stimulus strikes the retina. Br. J. Ophthal- mol. 32, 6, 340-346.
  54. VISHWANATH, D. 2008. The focal blur gradient affects perceived absolute distance [ECVP abstract supplement]. Perception 27, 130.
  55. WALLACH, H. AND NORRIS, C. M. 1963. Accommodation as a distance cue. American Journal of Psychology 76, 659-664.
  56. WATSON, J., BANKS, M., HOFSTEN, C., AND ROYDEN, C. S. 1992. Grav- ity as a monocular cue for perception of absolute distance and/or absolute size. Perception 21, 1, 69-76.
  57. WATT, S. J., AKELEY, K., ERNST, M. O., AND BANKS, M. S. 2005. Focus cues affect perceived depth. J. Vis. 5, 10 (12), 834-862.
  58. WILSON, B. J., DECKER, K. E., AND ROORDA, A. 2002. Monochromatic aberrations provide an odd-error cue to focus direction. Journal of the Optical Society of America A 19, 833-839.
  59. YAMANOUE, H. 1997. The relation between size distortion and shooting conditions for stereoscopic images. Journal of the SMPTE 106, 4, 225- 232.
  60. YAMANOUE, H., OKUI, M., AND YUYAMA, I. 2000. A study on the re- lationship between shooting conditions and cardboard effect of stereo- scopic images. Circuits and Systems for Video Technology, IEEE Trans- actions on 10, 3 (Apr), 411-416.

Related papers

The blur effect: perception and estimation with a new no-reference perceptual blur metric
Patricia Ladret

SPIE Proceedings, 2007

View PDFchevron_right
Quantifying how the combination of blur and disparity affects the perceived depth
Vincent Ricordel

2011

The influence of a monocular depth cue, blur, on the apparent depth of stereoscopic scenes will be studied in this paper. When 3D images are shown on a planar stereoscopic display, binocular disparity becomes a pre-eminent depth cue. But it induces simultaneously the conflict between accommodation and vergence, which is often considered as a main reason for visual discomfort. If we limit this visual discomfort by decreasing the disparity, the apparent depth also decreases. We propose to decrease the (binocular) disparity of 3D presentations, and to reinforce (monocular) cues to compensate the loss of perceived depth and keep an unaltered apparent depth. We conducted a subjective experiment using a twoalternative forced choice task. Observers were required to identify the larger perceived depth in a pair of 3D images with/without blur. By fitting the result to a psychometric function, we obtained points of subjective equality in terms of disparity. We found that when blur is added to the background of the image, the viewer can perceive larger depth comparing to the images without any blur in the background. The increase of perceived depth can be considered as a function of the relative distance between the foreground and background, while it is insensitive to the distance between the viewer and the depth plane at which the blur is added.

View PDFchevron_right
Retinal blur and the perception of egocentric distance
Erik Blaser

A central function of vision is determining the layout and size of objects in the visual field, both of which require knowledge of egocentric distance (the distance of an object from the observer). A wide range of visual cues can reliably signal relative depth relations among objects, but retinal signals directly specifying distance to an object are limited. A potential source of distance information is the pattern of blurring on the retina, since nearer fixation generally produces larger gradients of blur on the extra-foveal retina. While prior studies implicated blur as only a qualitative cue for relative depth ordering, we find that retinal blur gradients can act as a quantitative cue to distance. Surfaces depicted with blur gradients were judged as significantly closer than those without, with the size of the effect modulated by the degree of blur, as well as the availability of other extra-retinal cues to distance. Blur gradients produced substantial changes in perceived distance regardless of relative depth relations of the surfaces indicated by other cues, suggesting that it operates as a robust cue to distance, consistent with the empirical relationship between blur and fixation distance.

View PDFchevron_right
<title>Quantifying how the combination of blur and disparity affects the perceived depth</title>
Patrick Le Callet

Human Vision and Electronic Imaging XVI, 2011

The influence of a monocular depth cue, blur, on the apparent depth of stereoscopic scenes will be studied in this paper. When 3D images are shown on a planar stereoscopic display, binocular disparity becomes a pre-eminent depth cue. But it induces simultaneously the conflict between accommodation and vergence, which is often considered as a main reason for visual discomfort. If we limit this visual discomfort by decreasing the disparity, the apparent depth also decreases. We propose to decrease the (binocular) disparity of 3D presentations, and to reinforce (monocular) cues to compensate the loss of perceived depth and keep an unaltered apparent depth. We conducted a subjective experiment using a twoalternative forced choice task. Observers were required to identify the larger perceived depth in a pair of 3D images with/without blur. By fitting the result to a psychometric function, we obtained points of subjective equality in terms of disparity. We found that when blur is added to the background of the image, the viewer can perceive larger depth comparing to the images without any blur in the background. The increase of perceived depth can be considered as a function of the relative distance between the foreground and background, while it is insensitive to the distance between the viewer and the depth plane at which the blur is added.

View PDFchevron_right
The (In)Effectiveness of Simulated Blur for Depth Perception in Naturalistic Images
Peter Bex

PloS one, 2015

We examine depth perception in images of real scenes with naturalistic variation in pictorial depth cues, simulated dioptric blur and binocular disparity. Light field photographs of natural scenes were taken with a Lytro plenoptic camera that simultaneously captures images at up to 12 focal planes. When accommodation at any given plane was simulated, the corresponding defocus blur at other depth planes was extracted from the stack of focal plane images. Depth information from pictorial cues, relative blur and stereoscopic disparity was separately introduced into the images. In 2AFC tasks, observers were required to indicate which of two patches extracted from these images was farther. Depth discrimination sensitivity was highest when geometric and stereoscopic disparity cues were both present. Blur cues impaired sensitivity by reducing the contrast of geometric information at high spatial frequencies. While simulated generic blur may not assist depth perception, it remains possible ...

View PDFchevron_right

Cited by

Analyzing effects of geometric rendering parameters on size and distance estimation in on-axis stereographics
Gerd Bruder

Proceedings of the ACM Symposium on Applied Perception - SAP '12, 2012

Accurate perception of size and distance in a three-dimensional virtual environment is important for many applications. However, several experiments have revealed that spatial perception of virtual environments often deviates from the real world, even when the virtual scene is modeled as an accurate replica of a familiar physical environment. While previous research has elucidated various factors that can facilitate perceptual shifts, the effects of geometric rendering parameters on spatial cues are still not well understood.

View PDFchevron_right
Visual discomfort and depth-of-field
Paul B Hibbard

i-Perception, 2013

Visual discomfort has been reported for certain visual stimuli and under particular viewing conditions, such as stereoscopic viewing. In stereoscopic viewing, visual discomfort can be caused by a conflict between accommodation and convergence cues that may specify different distances in depth. Earlier research has shown that depth-of-field, which is the distance range in depth in the scene that is perceived to be sharp, influences both the perception of egocentric distance to the focal plane, and the distance range in depth between objects in the scene. Because depth-of-field may also be in conflict with convergence and the accommodative state of the eyes, we raised the question of whether depth-of-field affects discomfort when viewing stereoscopic photographs. The first experiment assessed whether discomfort increases when depth-of-field is in conflict with coherent accommodation-convergence cues to distance in depth. The second experiment assessed whether depth-of-field influences discomfort from a pre-existing accommodation-convergence conflict. Results showed no effect of depth-of-field on visual discomfort. These results suggest therefore that depth-of-field can be used as a cue to depth without inducing discomfort in the viewer, even when cue conflicts are large.

View PDFchevron_right
Evaluation of monocular depth cues on a high-dynamic-range display for visualization
Haider Easa

ACM Transactions on Applied Perception

The aim of this work is to identify the depth cues that provide intuitive depth-ordering when used to visualize abstract data. In particular we focus on the depth cues that are effective on a high-dynamic-range (HDR) display: contrast and brightness. In an experiment participants were shown a visualization of the volume layers at different depths with a single isolated monocular cue as the only indication of depth. The observers were asked to identify which slice of the volume appears to be closer. The results show that brightness, contrast and relative size are the most effective monocular depth cues for providing an intuitive depth ordering.

View PDFchevron_right
Perception-driven Accelerated Rendering
Martin Weier

Computer Graphics Forum

Advances in computer graphics enable us to create digital images of astonishing complexity and realism. However, processing resources are still a limiting factor. Hence, many costly but desirable aspects of realism are often not accounted for, including global illumination, accurate depth of field and motion blur, spectral effects, etc. especially in real-time rendering. At the same time, there is a strong trend towards more pixels per display due to larger displays, higher pixel densities or larger fields of view. Further observable trends in current display technology include more bits per pixel (high dynamic range, wider color gamut/fidelity), increasing refresh rates (better motion depiction), and an increasing number of displayed views per pixel (stereo, multi-view, all the way to holographic or lightfield displays). These developments cause significant unsolved technical challenges due to aspects such as limited compute power and bandwidth. Fortunately, the human visual system has certain limitations, which mean that providing the highest possible visual quality is not always necessary. In this report, we present the key research and models that exploit the limitations of perception to tackle visual quality and workload alike. Moreover, we present the open problems and promising future research targeting the question of how we can minimize the effort to compute and display only the necessary pixels while still offering a user full visual experience.

View PDFchevron_right
(Disparity-Driven) Accommodation Response Contributes to Perceived Depth
Justin Plantier

Frontiers in Neuroscience

When looking at objects at various distances in the physical space, the accommodation and vergence systems adjust their parameters to provide a single and clear vision of the world. Subtended muscle activity provides oculomotor cues that can contribute to the perception of depth and distance. While several studies have outlined the role of vergence in distance perception, little is known about the contribution of its concommitant accommodation component. It is possible to unravel the role of each of these physiological systems by placing observers in a situation where there is a conflict between accommodation and vergence distances. We thus sought to determine the contribution of each response system to perceived depth by simultaneously measuring vergence and accommodation while participants judged the depth of 3D stimuli. The distance conflict decreased depth constancy for stimulus displayed with negative disparity steps (divergence). Although vergence was unaffected by the stimulus distance, accommodation responses were significantly reduced when the stimulus was displayed with negative disparities. Our results show that biases in perceived depth follow undershoots in the disparity-driven accommodation response. These findings suggest that accommodation responses (i.e., from oculomotor information) can contribute to perceived depth.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved