Efficient hardware implementations of low bit depth motion estimation algorithms

In this paper, we present efficient hardware implementation of multiplication free one-bit transform (MF1BT) based and constraint one-bit transform (C-1BT) based motion estimation (ME) algorithms, in order to provide low bit-depth representation based full search block ME hardware for real-time video encoding. We used a source pixel based linear array (SPBLA) hardware architecture for low bit depth ME for the first time in the literature. The proposed SPBLA based implementation results in a genuine data flow scheme which significantly reduces the number of data reads from the current block memory, which in turn reduces the power consumption by at least 50% compared to conventional 1BT based ME hardware architecture presented in the literature. Because of the binary nature of low bit-depth ME algorithms, their hardware architectures are more efficient than existing 8 bits/pixel representation based ME architectures

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

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

Low complexity hardware oriented H.264/AVC motion estimation algorithm and related low power and low cost architecture design

制度:新 ; 報告番号:甲2999号 ; 学位の種類:博士(工学) ; 授与年月日:2010/3/15 ; 早大学位記番号:新525

Parallel H.264/AVC Fast Rate-Distortion Optimized Motion Estimation using Graphics Processing Unit and Dedicated Hardware

Heterogeneous systems on a single chip composed of CPU, Graphical Processing Unit (GPU), and Field Programmable Gate Array (FPGA) are expected to emerge in near future. In this context, the System on Chip (SoC) can be dynamically adapted to employ different architectures for execution of data-intensive applications. Motion estimation is one such task that can be accelerated using FPGA and GPU for high performance H.264/AVC encoder implementation. In most of works on parallel implementation of motion estimation, the bit rate cost of motion vectors is generally ignored. On the contrary, this paper presents a fast rate-distortion optimized parallel motion estimation algorithm implemented on GPU using OpenCL and FPGA/ASIC using VHDL. The predicted motion vectors are estimated from temporally preceding motion vectors and used for evaluating the bit rate cost of the motion vectors simultaneously. The experimental results show that the proposed scheme achieves significant speedup on GPU and FPGA, and has comparable ratedistortion performance with respect to sequential fast motion estimation algorithm