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Fig. 4. New Three Step Search block matching: Big circles are checking points in the first step of TSS and the squares are the extra 8 points added in the first step of NTSS. Triangles and diamonds are second step of NTSS showing 3 points and 5 points being checked when least weight in first step is at one of the 8 neighbors of window center.

Figure 4 New Three Step Search block matching: Big circles are checking points in the first step of TSS and the squares are the extra 8 points added in the first step of NTSS. Triangles and diamonds are second step of NTSS showing 3 points and 5 points being checked when least weight in first step is at one of the 8 neighbors of window center.

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Fig. 1. MPEG/ H.26x video compression process flow. sending a full frame that is JPEG encoded. Motion JPEG, where all frames are JPEG encoded, achieves anything between 10:1 to 15:1 compression ration, where as MPEG can achieve a compression ratio of 30:1 and is also useful at 100:1 ratio [1] [2] [3]. It should be noted that the first frame is always sent full, and so are some other frames that might occur at some regular interval (like every 6" frame). The standards do not specify this and this might change with every video being sent based on the dynamics of the video. Aroh Barjatya, Student Member, IEEE
Fig. 1. MPEG/ H.26x video compression process flow. sending a full frame that is JPEG encoded. Motion JPEG, where all frames are JPEG encoded, achieves anything between 10:1 to 15:1 compression ration, where as MPEG can achieve a compression ratio of 30:1 and is also useful at 100:1 ratio [1] [2] [3]. It should be noted that the first frame is always sent full, and so are some other frames that might occur at some regular interval (like every 6" frame). The standards do not specify this and this might change with every video being sent based on the dynamics of the video. Aroh Barjatya, Student Member, IEEE
Fig. 3. Three Step Search procedure. The motion vector is (5, -3). Peak-Signal-to-Noise-Ratio (PSNR) given by equation (iii) characterizes the motion compensated image that is created by using motion vectors and macro clocks from the reference frame.
Fig. 3. Three Step Search procedure. The motion vector is (5, -3). Peak-Signal-to-Noise-Ratio (PSNR) given by equation (iii) characterizes the motion compensated image that is created by using motion vectors and macro clocks from the reference frame.
Fig. 2. Block Matching a macro block of side 16 pixels and a search parameter p of size 7 pixels. The underlying supposition behind motion estimation is that the patterns corresponding to objects and background in a frame of video sequence move within the frame to form corresponding objects on the subsequent frame. The idea behind block matching is to divide the current frame into a matrix of ‘macro blocks’ that are then compared with corresponding block and its adjacent neighbors in the previous frame to create a vector that stipulates the movement of a macro block from one location to another in the previous frame. This movement calculated for all the macro blocks comprising a frame, constitutes the motion estimated in the current frame. The search area for a good macro block match is constrained up to p pixels on all fours sides of the corresponding macro block in previous frame. This ‘p’ is called as the search parameter. Larger motions require a larger p, and the larger the search parameter the more computationally expensive the process of motion estimation becomes. Usually the macro block is taken as a square of side 16 pixels, and the search parameter p is 7 pixels. The idea is represented in Fig 2. The matching of one macro block with another is based on the output of a cost function. The macro block that results in the least cost is the one that matches the closest to current block. There are various cost functions, of which the most popular and less computationally expensive is Mean Absolute Difference (MAD) given by equation (i). Another cost function is Mean Squared Error (MSE) given by equation (ii). The underlying supposition behind motion estimation is that
Fig. 2. Block Matching a macro block of side 16 pixels and a search parameter p of size 7 pixels. The underlying supposition behind motion estimation is that the patterns corresponding to objects and background in a frame of video sequence move within the frame to form corresponding objects on the subsequent frame. The idea behind block matching is to divide the current frame into a matrix of ‘macro blocks’ that are then compared with corresponding block and its adjacent neighbors in the previous frame to create a vector that stipulates the movement of a macro block from one location to another in the previous frame. This movement calculated for all the macro blocks comprising a frame, constitutes the motion estimated in the current frame. The search area for a good macro block match is constrained up to p pixels on all fours sides of the corresponding macro block in previous frame. This ‘p’ is called as the search parameter. Larger motions require a larger p, and the larger the search parameter the more computationally expensive the process of motion estimation becomes. Usually the macro block is taken as a square of side 16 pixels, and the search parameter p is 7 pixels. The idea is represented in Fig 2. The matching of one macro block with another is based on the output of a cost function. The macro block that results in the least cost is the one that matches the closest to current block. There are various cost functions, of which the most popular and less computationally expensive is Mean Absolute Difference (MAD) given by equation (i). Another cost function is Mean Squared Error (MSE) given by equation (ii). The underlying supposition behind motion estimation is that
where N is the side of the macro bock, Cj and Rj are the pixels being compared in current macro block and reference macro block, respectively.
where N is the side of the macro bock, Cj and Rj are the pixels being compared in current macro block and reference macro block, respectively.
Fig. 5. Search patterns corresponding to each selected quadrant: (a) Shows all quadrants (b) quadrant I is selected (c) quadrant II is selected (d) quadrant II is selected (e) quadrant IV is selected
Fig. 5. Search patterns corresponding to each selected quadrant: (a) Shows all quadrants (b) quadrant I is selected (c) quadrant II is selected (d) quadrant II is selected (e) quadrant IV is selected
Fig. 6. The SES procedure. The motion vector is (3, 7) in this example.
Fig. 6. The SES procedure. The motion vector is (3, 7) in this example.
Fig. 8. Four Step Search procedure. The motion vector is (3, -7). Fig. 7. Search patterns of the FSS. (a) First step (b) Second/Third step (c)Second/Third Step (d) Fourth Step Once we have selected the points to check for in second phase, we find the location with the lowest weight and set it as the origin. We then change the step size similar to TSS and repeat the above SES procedure again until we reach S = 1. The location with the lowest weight is then noted down in terms of motion vectors and transmitted. An example process is illustrated in Fig 6.
Fig. 8. Four Step Search procedure. The motion vector is (3, -7). Fig. 7. Search patterns of the FSS. (a) First step (b) Second/Third step (c)Second/Third Step (d) Fourth Step Once we have selected the points to check for in second phase, we find the location with the lowest weight and set it as the origin. We then change the step size similar to TSS and repeat the above SES procedure again until we reach S = 1. The location with the lowest weight is then noted down in terms of motion vectors and transmitted. An example process is illustrated in Fig 6.
Fig. 9. Diamond Search procedure. This figure shows the large diamond search pattern and the small diamond search pattern. It also shows an example path to motion vector (-4, -2) in five search steps- four times of LDSP and one time of SDSP.
Fig. 9. Diamond Search procedure. This figure shows the large diamond search pattern and the small diamond search pattern. It also shows an example path to motion vector (-4, -2) in five search steps- four times of LDSP and one time of SDSP.
Fig. 11. Search points per macro block while computing the PSNR performance of Fast Block Matching Algorithms.
Fig. 11. Search points per macro block while computing the PSNR performance of Fast Block Matching Algorithms.
Fig. 10. Adaptive Root Pattern: The predicted motion vector is (3,-2) , and the step size S = Max( 13l, |-2I) = 3.
Fig. 10. Adaptive Root Pattern: The predicted motion vector is (3,-2) , and the step size S = Max( 13l, |-2I) = 3.
Fig. 12. PSNR performance of Fast Block Matching Algorithms. Caltrain Sequence was used with a frame distance of 2.
Fig. 12. PSNR performance of Fast Block Matching Algorithms. Caltrain Sequence was used with a frame distance of 2.
Fig. 13. Cross Search Pattern: This pattern is used by CDS, SCDS, and NCDS as their search start pattern. While CDS uses all 9 points, SCDS and NCDS use only the inner 5 points. worst PSNR performance. Although PSNR performance of 4SS, DS, and ARPS is relatively the same, ARPS takes a factor of 2 less computations and hence is the best of the fast block matching algorithms studied in this paper. The results are similar to that of [8].
Fig. 13. Cross Search Pattern: This pattern is used by CDS, SCDS, and NCDS as their search start pattern. While CDS uses all 9 points, SCDS and NCDS use only the inner 5 points. worst PSNR performance. Although PSNR performance of 4SS, DS, and ARPS is relatively the same, ARPS takes a factor of 2 less computations and hence is the best of the fast block matching algorithms studied in this paper. The results are similar to that of [8].
Related topics:
Video CompressionMotion estimation
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