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Motion estimation (ME) is an important part of modern video coding systems to exploit temporary redundancy in a video. Motion estimation is typically per-formed firstly with integer-pixel accuracy and then at sub-pixel accuracy, which includes half-pixel and quarter-pixel accuracy. When sophisticated fast integer-pixel accuracy motion-search algori...
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... I be the integer-pixel position obtained from integer-pixel motion search. Let h1, h2, h3, …, h8 be the eight half-pixel positions around I. FQPS computes the SAD for all these 8 positions as shown in Fig. 1 and find the best one, say h2, with lowest SAD. Then it computes the SAD for the eight quarter-pixel positions q1, q2, q3, …, q8 around the best half-pixel location. Finally, the position, say q8, with lowest SAD is chosen. Step ...
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Citations
SAD(Sum of Absolute Difference) is commonly used as the error estimate to identify the most similar block when trying to obtain the block motion vector in the process of motion estimation, which requires only easy calculation without the need for multiplication. Generally, the probability of the minimum SAD values is high when searching point is in the distance one pixel from the reference point. Thus, we reduced the searching area and then we can overcome the computational complexity problem. The main concept of proposed algorithm, which based on TSS(Three Step Search) method, first we find three minimum SAD points which is in integer distance unit, and then, in second step, the optimal point is in 1/2-pixel unit either between the most minimum SAD value point and the second minimum SAD point or between the most minimum SAD value point and the third minimum SAD point In third step, after finding the smallest SAD value between two SAD values on 1/2-pixel unit, the final optimized point is between the most minimum SAD value and the result value of the third step, in 1/2-pixel unit i.e., 1/4-pixel unit in totally. The conventional TSS method needs an eight search points in the sub-pixel steps in 1/2-pixel unit and also an eight search points in 1/4-pixel, to detect the optimal point, however the method suggested in this paper may detect only 5 search points.
The registration error is very important since it affects to final high resolution (HR) image quality. In this paper, we present the improved method to obtain high quality super resolution (SR) reconstruction using auto select input image algorithm. We took the threshold value from maximum motion compensation error (MMCE) to evaluate the suitability of the input image candidates. If the motion compensation error (MCE) between input low resolution (LR) image and reference input LR image is in range of 0 < MCE < MMCE then that input LR image is selected and otherwise that input LR image are neglected. And we selected the reference input LR (SRILR) image by comparing the number of the selected input LR (SILR) images for each reference input LR (RILR) images. Finally, the HR image is generated from restoration and reconstruction block which based on the Hardie algorithm by using SRILR and SILR images. Our proposal algorithm is expected to improve the quality of super resolution (SR) without user participation and it will apply to moving picture.
Motion estimation (ME) is a core part of modern video coding systems to exploit temporary redundancy in a video. Motion estimation is typically performed firstly with integer pixel accuracy and then at sub-pixel accuracy, which includes half-pixel and quarter-pixel accuracy. When sophisticated fast integer-pixel accuracy motion-search algorithms are used to decrease the number of search points for integer-pixel motion search, thus sub-pixel motion search became another important processing bottleneck in the encoding process. The conventional method is to search 8 half-pixel positions around the motion vector (MV) obtained from integer pixel motion search, then do motion search in the same way on 8 quarter-pixel positions around the MV obtained from half-pixel motion search, therefore totally 16 search points are needed. The proposed algorithm, fast sub-pixel inter-prediction - based on texture direction analysis (FSIP-BTDA), successfully optimizes the sub-pixel motion search on both half and quarter-pixel accuracy, and improves the processing speed with low PSNR penalty.
The motion estimation is the most important technique in the image compression of the video standards. In the case of next generation standards in the video codec as H.264, a high compression-efficiency can be also obtained by using a motion compensation. To obtain the accurate motion search, a motion estimation should be achieved up to 1/2 pixel and 1/4 pixel units. To do this, the computational complexity is increased although the image compression rate is increased. Therefore, in this paper, we propose the advanced sub-pixel block matching algorithm to reduce the computational complexity by using statistical characteristics of SAD (sum of absolute difference). Generally, the probability of the minimum SAD values is high when searching point is in the distance 1 from the reference point. Thus, we reduced the searching area and then we can overcome the computational complexity problem. The main concept of proposed algorithm, which based on TSS (three step search) method, first we find three minimum SAD points which is in integer distance unit, and then, in second step, the optimal point is in 1/2 pixel unit either between the most minimum SAD value point and the second minimum SAD point or between the most minimum SAD value point and the third minimum SAD point In third step, after finding the smallest SAD value between two SAD values on 1/2 pixel unit, the final optimized point is between the most minimum SAD value and the result value of the third step, in 1/2 pixel unit i.e., 1/4 pixel unit in totally. The conventional TSS method needs an eight search points in the sub-pixel steps in 1/2 pixel unit and also an eight search points in 1/4 pixel, to detect the optimal point. However, in proposed algorithm, only total five search points are needed. In the result, 22% improvement of processing speed is obtained.
