HDS, a real-time multi-DSP motion estimator for MPEG-4 H.264 AVC high definition video encoding

International audienceH.264 AVC video compression standard achieves high compression rates at the cost of a high encoder complexity. The encoder performances are greatly linked to the motion estimation operation which requires high computation power and memory bandwidth. High definition context magnifies the difficulty of a real-time implementation. EPZS and HME are two well-known motion estimation algorithms. Both EPZS and HME are implemented in a DSP and their performances are compared in terms of both quality and complexity. Based on these results, a new algorithm called HDS for Hierarchical Diamond Search is proposed. HDS motion estimation is integrated in a AVC encoder to extract timings and resulting video qualities reached. A real-time DSP implementation of H.264 quarter-pixel accuracy motion estimation is proposed for SD and HD video format. Furthermore HDS characteristics make this algorithm well suited for H.264 SVC real-time encoding applications

Optimization of the motion estimation for parallel embedded systems in the context of new video standards

15 pagesInternational audienceThe effciency of video compression methods mainly depends on the motion compensation stage, and the design of effcient motion estimation techniques is still an important issue. An highly accurate motion estimation can significantly reduce the bit-rate, but involves a high computational complexity. This is particularly true for new generations of video compression standards, MPEG AVC and HEVC, which involves techniques such as different reference frames, sub-pixel estimation, variable block sizes. In this context, the design of fast motion estimation solutions is necessary, and can concerned two linked aspects: a high quality algorithm and its effcient implementation. This paper summarizes our main contributions in this domain. In particular, we first present the HME (Hierarchical Motion Estimation) technique. It is based on a multi-level refinement process where the motion estimation vectors are first estimated on a sub-sampled image. The multi-levels decomposition provides robust predictions and is particularly suited for variable block sizes motion estimations. The HME method has been integrated in a AVC encoder, and we propose a parallel implementation of this technique, with the motion estimation at pixel level performed by a DSP processor, and the sub-pixel refinement realized in an FPGA. The second technique that we present is called HDS for Hierarchical Diamond Search. It combines the multi-level refinement of HME, with a fast search at pixel-accuracy inspired by the EPZS method. This paper also presents its parallel implementation onto a multi-DSP platform and the its use in the HEVC context

A flexible heterogeneous hardware/software solution for real-time high-definition H.264 motion estimation

International audienceThe MPEG-4 AVC/H.264 video compression standard introduces a high degree of motion estimation complexity. Quarter-pixel accuracy and variable block-size significantly enhance compression performances over previous standards, but increase computation requirements. Firstly, a DSP-based solution achieves real-time integer motion estimation. Nevertheless, fractional-pixel refinement is too computationally intensive to be efficiently processed on a software-based processor. Secondly, to address this restriction, a flexible and low complexity VLSI sub-pixel refinement coprocessor is designed. Thanks to an improved datapath, a high throughput is achieved with low logic resources. Finally, we propose a heterogeneous (DSP-FPGA) solution to handle real-time motion estimation with variable block-size and fractional-pixel accuracy for high-definition video. It combines efficiency and programmability. The flexibility offers complexity versus performance trade-offs. The system achieves motion estimation of 720p sequences at up to 60 frames per second

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

Block Matching Algorithms for the Estimation of Motion in Image Sequences: Analysis

Several video coding standards and techniques have been introduced for multimedia applications, particularly the h.26x series for video processing. These standards employ motion estimation processing to reduce the amount of data that is required to store or transmit the video. The motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search, many fast, and fast full search block-based algorithms has been presented. An evaluation of up-to-date motion estimation algorithms, based on several empirical results on several test video sequences, is presented as well

Block matching algorithm based on Harmony Search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate.Comment: 25 Pages. arXiv admin note: substantial text overlap with arXiv:1405.472