Reconfigurable Architecture For H.264/avc Variable Block Size Motion Estimation Based On Motion Activity And Adaptive Search Range

Motion Estimation (ME) technique plays a key role in the video coding systems to achieve high compression ratios by removing temporal redundancies among video frames. Especially in the newest H.264/AVC video coding standard, ME engine demands large amount of computational capabilities due to its support for wide range of different block sizes for a given macroblock in order to increase accuracy in finding best matching block in the previous frames. We propose scalable architecture for H.264/AVC Variable Block Size (VBS) Motion Estimation with adaptive computing capability to support various search ranges, input video resolutions, and frame rates. Hardware architecture of the proposed ME consists of scalable Sum of Absolute Difference (SAD) arrays which can perform Full Search Block Matching Algorithm (FSBMA) for smaller 4x4 blocks. It is also shown that by predicting motion activity and adaptively adjusting the Search Range (SR) on the reconfigurable hardware platform, the computational cost of ME required for inter-frame encoding in H.264/AVC video coding standard can be reduced significantly. Dynamic Partial Reconfiguration is a unique feature of Field Programmable Gate Arrays (FPGAs) that makes best use of hardware resources and power by allowing adaptive algorithm to be implemented during run-time. We exploit this feature of FPGA to implement the proposed reconfigurable architecture of ME and maximize the architectural benefits through prediction of motion activities in the video sequences ,adaptation of SR during run-time, and fractional ME refinement. The implemented ME architecture can support real time applications at a maximum frequency of 90MHz with multiple reconfigurable regions. iv When compared to reconfiguration of complete design, partial reconfiguration process results in smaller bitstream size which allows FPGA to implement different configurations at higher speed. The proposed architecture has modular structure, regular data flow, and efficient memory organization with lower memory accesses. By increasing the number of active partial reconfigurable modules from one to four, there is a 4 fold increase in data re-use. Also, by introducing adaptive SR reduction algorithm at frame level, the computational load of ME is reduced significantly with only small degradation in PSNR (≤0.1dB)

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Fast Motion Estimation Algorithms for Block-Based Video Coding Encoders

The objective of my research is reducing the complexity of video coding standards in real-time scalable and multi-view applications

Implementing video compression algorithms on reconfigurable devices

The increasing density offered by Field Programmable Gate Arrays(FPGA), coupled with their short design cycle, has made them a popular choice for implementing a wide range of algorithms and complete systems. In this thesis the implementation of video compression algorithms on FPGAs is studied. Two areas are specifically focused on; the integration of a video encoder into a complete system and the power consumption of FPGA based video encoders. Two FPGA based video compression systems are described, one which targets surveillance applications and one which targets video conferencing applications. The FPGA video surveillance system makes use of a novel memory format to improve the efficiency with which input video sequences can be loaded over the system bus. The power consumption of a FPGA video encoder is analyzed. The results indicating that the motion estimation encoder stage requires the most power consumption. An algorithm, which reuses the intra prediction results generated during the encoding process, is then proposed to reduce the power consumed on an FPGA video encoder’s external memory bus. Finally, the power reduction algorithm is implemented within an FPGA video encoder. Results are given showing that, in addition to reducing power on the external memory bus, the algorithm also reduces power in the motion estimation stage of a FPGA based video encoder

Transforms for prediction residuals in video coding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-140).Typically the same transform, the 2-D Discrete Cosine Transform (DCT), is used to compress both image intensities in image coding and prediction residuals in video coding. Major prediction residuals include the motion compensated prediction residual, the resolution enhancement residual in scalable video coding, and the intra prediction residual in intra-frame coding. The 2-D DCT is efficient at decorrelating images, but the spatial characteristics of prediction residuals can be significantly different from the spatial characteristics of images, and developing transforms that are adapted to the characteristics of prediction residuals can improve their compression efficiency. In this thesis, we explore the differences between the characteristics of images and prediction residuals by analyzing their local anisotropic characteristics and develop transforms adapted to the local anisotropic characteristics of some types of prediction residuals. The analysis shows that local regions in images have 2-D anisotropic characteristics and many regions in several types of prediction residuals have 1-D anisotropic characteristics. Based on this insight, we develop 1-D transforms for these residuals. We perform experiments to evaluate the potential gains achievable from using these transforms within the H.264 codec, and the experimental results indicate that these transforms can increase the compression efficiency of these residuals.by Fatih Kamışlı.Ph.D

Fast motion estimation algorithms for block-based video coding encoders

The objective of my research is reducing the complexity of video coding standards in real-time scalable and multi-view applications.EThOS - Electronic Theses Online ServiceGBUnited Kingdo