Exhaustive Block Matching Algorithm Research Articles

English

Movement of hands or camera while capturing video leads to unstable video. To stabilize those unstable videos many approaches are proposed. Out of those, Digital Video Stabilization consists of Motion Estimation, Padding & Histogram Equalisation and Motion Compensation. In the block based method the frame is divided into 16*16 square block called Macroblock. Motion Estimation is the process of finding particular macroblock of previous frame into current frame. For this block matching criterion are used. This paper uses Sum of Absolute Difference (SAD). Block Matching Algorithms suggests where to apply this SAD in the macroblock. This paper uses Full Search or Exhaustive Block matching Algorithm (EBMA). Using this block matching algorithm, motion vectors are calculated. Padding is the process of keeping these motion vectors within certain range. Histogram equalisation of motion vectors try to uniform these motion vectors.In motion compensation macroblocks are adjusted according to motion vectors to obtain stabilized frame.

Motion vector generation for video coding by gray prediction

An efficient prediction algorithm for motion vectors in video compression is proposed. Motion estimation is an important part in motion compensated video encoding system, which is used to eliminate the temporal redundancy in video sequence. Motion vectors dominate the quality of decoded video so that the generation of motion vectors is an important topic in video coding. Exhaustive block matching algorithm (EBMA) can get the optimal solution for motion vectors, but it takes much computational burden. In the proposed method, the authors use gray prediction to obtain the motion vectors. Gray prediction can predict the motion vectors quickly and accurately. Several video sequences are used to evaluate the performance in this study, and the experimental results show that the time that the proposed method needed is only 1.93% compared to EBMA and 11.75% compared to three steps searching algorithm whereas the degradation of PSNRY compared to EBMA is about 0.43 dB at most, which is almost invisible to human eyes.

A Computational Memory Architecture for MPEG-4 Applications with Mobile Devices

This paper presents a Computational Memory architecture for MPEG-4 applications with mobile devices. The proposed architecture is used for real-time block-based motion estimation, which is the most computational intensive task in the video encoder. It uses the exhaustive block-matching algorithm (EBMA) for motion estimation. The proposed architecture consists of embedded SRAMs and a number of block-matching units working in parallel to process video data while stored in the memory. The block-matching units access the embedded SRAMs simultaneously, which increases the speed of the architecture. The architecture processes CIF format video sequences (i.e., the frame size is 352 � 288 pixels) with block size of 16 � 16 pixels and �15 pixels search range. The proposed architecture has been designed, prototyped, and simulated for 0.18 �m TSMC CMOS technology. The simulation shows that the proposed architectures processes up to 126 CIF frames per second with clock frequency 100 MHz. The synthesized prototype of the proposed architecture includes 200 KB memory and it has an area of 33.75 mm2 and consumes 986.96 mW @100 MHz.

Evaluation of mesh-based motion estimation in H.263-like coders

We present two mesh-based motion estimation algorithms, and evaluate their performance when incorporated in an H.263-like block-based video coder. Both algorithms compute nodal motions in a hierarchical manner. Within each hierarchy level, the first algorithm (HMMA) minimizes the prediction error in the four elements surrounding each node, where the prediction is accomplished by a bilinear mapping. The optimal solution is obtained by a full search within a range defined by the topology of the mesh. The second algorithm (HBMA) minimizes the error in a block surrounding each node, assuming the motion in the block is constant. In both cases, bilinear mapping is used for motion-compensated prediction based on nodal displacements. The two algorithms are compared with an exhaustive block-matching algorithm (EBMA) by evaluating their performance in temporal prediction and in an H.263/TMN4 coder. For prediction only, the HMMA and HBMA algorithms yield visually more satisfactory results, even though the PSNRs of the predicted images are on average lower. The coded images also have lower PSNRs at similar bit rates. The coding artifacts are different: while the block-based method leads to more severe block distortions, the mesh-based method experiences some warping artifacts. The HMMA algorithm outperforms the HBMA slightly for certain sequences at the expense of higher computational complexity.