Interlinear image interpolation scheme for real time application

First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)

In this paper, an adaptive interpolation algorithm is presented based on the Newton Polynomial to improve the limitation of the traditional algorithm for image resizing. The second-order difference of adjacent pixels gray values shows the relativity among the pixels. Accordingly, the adaptive function for image interpolation is deduced according to both this relativity and the classical Newton polynomial. Then the efficiency of our method is compared with that of the traditional algorithm for image resizing in Matlab. Furthermore, the implementation circuit architecture is devised by three stage paralleling pipelines for the adaptive image resizing algorithm and is verified in FPGA (field programmable gate array). The experimental results show that our proposed algorithm excels the bicubic interpolation in visual effect, and has a lower complexity. Therefore, the algorithm adapts to real-time image resizing.

IJARCCE, 2016

Image interpolation is the most basic requirement for many image processing task such as computer graphics, gaming, medical image processing, virtualization, camera surveillance and quality control. Image interpolation is a technique used in resizing images. To resize an image, every pixel in the new image must be mapped back to a location in the old image in order to calculate a value of new pixel. There are many algorithms available for determining new value of the pixel, most of which involve some form of interpolation among the nearest pixels in the old image. In this paper, we used Nearest-neighbor, Bilinear, Bicubic, Bicubic B-spline, Catmull-Rom, Mitchell-Netravali and Lanzcos of order three algorithms for image interpolation. Each algorithms generates varies artifact such as aliasing, blurring and moiré. This paper presents, quality and computational time consideration of images while using these interpolation algorithms.

ieeexplore.ieee.org

Recently a lot of interpolation algorithms are proposed, but these interpolation algorithms are highly computationally expensive. Hence these algorithms cannot be implemented and used in real time applications. In view of real time applications we have proposed a computationally simple interpolation algorithm. In our proposed algorithm the unknown pixels are categorized into various bins depending upon the characteristic of the neighboring pixels (activity level) and for each bin fixed prediction parameters are used for prediction. We have presented different set of fixed predictors for both smooth type and edgy type of images. We have also proposed a modified algorithm in which selection of prediction parameter is done on block by block basis instead of image basis. Our proposed algorithm gives much better qualitative and quantitative performance as compared to other computationally simple interpolation algorithms.

Optoelectronic Imaging and Multimedia Technology V

Unlike traditional linear interpolation algorithms, which compute all kernel pixels locations, a novel image interpolation algorithm that uses the preliminary pixels kernel and extrapolated pixels adjustment has been proposed for interpolation operations. The proposed interpolation algorithm is mainly based on the weighting functions of the preliminary interpolation kernel and linearly extrapolated pixels adjustments. Experimentally, the proposed method demonstrated generally higher performance than state-of-art algorithms mentioned with objective evaluations as well as comparable performances with subjective evaluations. Potential applications may include the ultrasound scan conversion for displaying the sectored image.

Journal of Information Processing Systems, 2006

In this paper, we propose an image interpolator for low computational complexity. The proposed image interpolator supports the image scaling using a modified cubic convolution interpolation between the input and output resolutions for a full screen display. In order to reduce the computational complexity, we use the difference in value of the adjacent pixels for selecting interpolation methods and linear function of the cubic convolution. The proposed image interpolator is compared with the conventional one for the computational complexity and image quality. The proposed image interpolator has been designed and verified by Verilog HDL(Hardware Description Language). It has been synthesized using the Xilinx VirtexE FPGA, and implemented using an FPGA-based prototype board.