Motion Estimation In Video Compression Research Articles

High-Performance Motion Estimation for Image Sensors with Video Compression.

It is important to reduce the time cost of video compression for image sensors in video sensor network. Motion estimation (ME) is the most time-consuming part in video compression. Previous work on ME exploited intra-frame data reuse in a reference frame to improve the time efficiency but neglected inter-frame data reuse. We propose a novel inter-frame data reuse scheme which can exploit both intra-frame and inter-frame data reuse for ME in video compression (VC-ME). Pixels of reconstructed frames are kept on-chip until they are used by the next current frame to avoid off-chip memory access. On-chip buffers with smart schedules of data access are designed to perform the new data reuse scheme. Three levels of the proposed inter-frame data reuse scheme are presented and analyzed. They give different choices with tradeoff between off-chip bandwidth requirement and on-chip memory size. All three levels have better data reuse efficiency than their intra-frame counterparts, so off-chip memory traffic is reduced effectively. Comparing the new inter-frame data reuse scheme with the traditional intra-frame data reuse scheme, the memory traffic can be reduced by 50% for VC-ME.

High Performances Block Matching Algorithms for Motion Estimation

This paper is a review of the block matching algorithms used for motion estimation in video compression. It implements and compares 8 different types of block matching algorithms that range from the very basic Exhaustive Search to the recent fast adaptive algorithms like Adaptive Rood Pattern Search. The algorithms that are evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards, ranging from MPEG1 / H.261 to MPEG4 / H.263. The paper also presents a very brief introduction to the entire flow of video compression.

A generalized model for Kalman filtering of motion information in video compression

In this paper, we tackle the problem of motion estimation in video compression. Since Full Search Algorithms (FSA) present the disadvantage of adding a high computational burden to the encoder, fast search techniques have been used in conjunction with predictive filtering, in such a way to guarantee an acceptable quality with an affordable complexity. The aim of this work is to propose a novel framework for Kalman filtering of motion information in compressed video sequences. The merits of our new framework are twofold: First, using an appropriate formulation of the system equations, several shortcomings inherent with former models in the literature are greatly counteracted. Secondly, it is constructed using a generalized structure in such a way to enclose a large variety of prediction models. Therefore, it can adapt to different types of motion activities in video sequences, without the need for a different formulation in each prediction model, as was the case in previous studies. Furthermore, we propose an adaptive motion compensation technique that permits an additional improvement to the decoded video quality. Our framework permits a considerable gain in the average performance compared to previous models and even to the FSA technique.

Specification of efficient block-matching scheme for motion estimation in video compression

We present an adaptive algorithm that finds the best block-matching results in a computationally constrained and varied environment. The conventional diamond search algorithm, though faster than most known algorithms, is not very robust for sequences with scene variations or significant global motion. To solve this issue, rather than only using one fast motion estimation algorithm, we devise a more adaptive selection of fast motion estimation algorithms. Our adaptive selection approach for fast block search (ASFBS) algorithm uses a diamond search and two new subalgorithms: a cross-three-step search algorithm for large moving images and an advanced cross-diamond search algorithm for small moving images. The proposed ASFBS adapts based on the length of the motion vector, the number of search points, and the matching criteria of the neighboring block. Experimental results show that ASFBS is much more robust; it is faster than other popular fast block-matching algorithms, with smaller distortions.

A New Perspective to Block Motion Estimation for Video Compression: High-Frequency Component Matching

This letter presents a new perspective to block motion estimation for video compression referred to as high-frequency component matching. Taking into consideration the features of the transform encoder used to encode the prediction error in the motion-compensated predictive coding system, motion estimation is carried out such as to compensate for high-frequency components, leaving the compensation task of low-frequency components to the transform encoder. It is shown that this approach can outperform standard motion estimation that does not take transform encoder features into account and has the potential of changing the way motion estimation is being performed in video compression standards

Fast gradient methods based on global motion estimation for video compression

This paper presents a fast global motion estimation (GME) algorithm based on gradient methods (GM), which can be used for real-time applications, such as in MPEG4 video compression. This approach improves the existing state-of-the-art GME algorithms by introducing two major modifications: first, only a small subset (down to 3%) of the original image pixels is used in the estimation process. Second, an interpolation-free formulation of the basic GM is derived, further decreasing the computational complexity. Experimental results show no loss of GME accuracy and compression efficiency compared to the MPEG-4 verification model, while reducing the computational complexity of the GME by a factor of 20.

An optimal quadtree-based motion estimation and motion-compensated interpolation scheme for video compression

We propose an optimal quadtree (QT)-based motion estimator for video compression. It is optimal in the sense that for a given bit budget for encoding the displacement vector field (DVF) and the QT segmentation, the scheme finds a DVF and a QT segmentation which minimizes the energy of the resulting displaced frame difference (DFD). We find the optimal QT decomposition and the optimal DVF jointly using the Lagrangian multiplier method and a multilevel dynamic program. We introduce a new, very fast convex search for the optimal Lagrangian multiplier lambda(*), which results in a very fast convergence of the Lagrangian multiplier method. The resulting DVF is spatially inhomogeneous, since large blocks are used in areas with simple motion and small blocks in areas with complex motion. We also propose a novel motion-compensated interpolation scheme which uses the same mathematical tools developed for the QT-based motion estimator. One of the advantages of this scheme is the globally optimal control of the tradeoff between the interpolation error energy and the DVF smoothness. Another advantage is that no interpolation of the DVF is required since we directly estimate the DVF and the QT-segmentation for the frame which needs to be interpolated. We present results with the proposed QT-based motion estimator which show that for the same DFD energy the proposed estimator uses about 25% fewer bits than the commonly used block matching algorithm. We also experimentally compare the interpolated frames using the proposed motion compensated interpolation scheme with the reconstructed original frames.

One-dimensional full search motion estimation algorithm for video coding

A new hardware-oriented algorithm called the one-dimensional full search (1DFS) is presented for block-matching motion estimation in video compression. The simulation for this algorithm follows H.261 and MPEG international standards. In MPEG simulation, structures with 1-, 2- and 3-frame interpolation are compared. The performance of 1DFS is superior to that of other fast search algorithms. And it has more regular data flow, data reuse and less control overhead. It is an alternative for 2D full search block matching and achieves a good compromise between computational complexity and performance. With competent performance and reasonable computation complexity, the proposed method is more suitable for real-time hardware realization of a VLSI motion estimator for video applications. >

Genetic motion search algorithm for video compression

A new approach to block-based motion estimation for video compression, called the genetic motion search (GMS) algorithm, is introduced. It makes use of a natural processing concept called genetic algorithm (GA). In contrast to existing fast algorithms, which rely on the assumption that the matching error decreases monotonically as the search point moves closer to the global optimum, the GMS algorithm is not fundamentally limited by this restriction. Experimental results demonstrate that GMS is more robust than other algorithms in locating the global optimum and is computationally simpler than the full search algorithm. GMS is also suitable for VLSI implementation because of its regularity and high architectural parallelism. >