Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such as H.264/AVC, VC-1. However, a low complexity H.264/AVC encoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D) optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region

Complexity adaptation in H.264/AVC video coder for static cameras

H.264/AVC uses variable block size motion estimation (VBSME) to improve coding gain. However, its complexity is significant and fixed regardless of the required quality or of the scene characteristics. In this paper, we propose an adaptive complexity algorithm based on using the Walsh Hadamard Transform (WHT). VBS automatic partition and skip mode detection algorithms also are proposed. Experimental results show that 70% - 5% of the computation of H.264/AVC is required to achieve the same PSNR

Complexity adaptation in video encoders for power limited platforms

With the emergence of video services on power limited platforms, it is necessary to consider both performance-centric and constraint-centric signal processing techniques. Traditionally, video applications have a bandwidth or computational resources constraint or both. The recent H.264/AVC video compression standard offers significantly improved efficiency and flexibility compared to previous standards, which leads to less emphasis on bandwidth. However, its high computational complexity is a problem for codecs running on power limited plat- forms. Therefore, a technique that integrates both complexity and bandwidth issues in a single framework should be considered. In this thesis we investigate complexity adaptation of a video coder which focuses on managing computational complexity and provides significant complexity savings when applied to recent standards. It consists of three sub functions specially designed for reducing complexity and a framework for using these sub functions; Variable Block Size (VBS) partitioning, fast motion estimation, skip macroblock detection, and complexity adaptation framework. Firstly, the VBS partitioning algorithm based on the Walsh Hadamard Transform (WHT) is presented. The key idea is to segment regions of an image as edges or flat regions based on the fact that prediction errors are mainly affected by edges. Secondly, a fast motion estimation algorithm called Fast Walsh Boundary Search (FWBS) is presented on the VBS partitioned images. Its results outperform other commonly used fast algorithms. Thirdly, a skip macroblock detection algorithm is proposed for use prior to motion estimation by estimating the Discrete Cosine Transform (DCT) coefficients after quantisation. A new orthogonal transform called the S-transform is presented for predicting Integer DCT coefficients from Walsh Hadamard Transform coefficients. Complexity saving is achieved by deciding which macroblocks need to be processed and which can be skipped without processing. Simulation results show that the proposed algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. Finally, a complexity adaptation framework which combines all three techniques mentioned above is proposed for maximizing the perceptual quality of coded video on a complexity constrained platform

Fast pattern matching in Walsh-Hadamard domain and its application in video processing.

Li Ngai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references.Abstracts in English and Chinese.Chapter Chapter 1. --- Introduction --- p.1-1Chapter 1.1. --- A Brief Review on Pattern Matching --- p.1-1Chapter 1.2. --- Objective of the Research Work --- p.1-5Chapter 1.3. --- Organization of the Thesis --- p.1-6Chapter 1.4. --- Notes on Publications --- p.1-7Chapter Chapter 2. --- Background Information --- p.2-1Chapter 2.1. --- Introduction --- p.2-1Chapter 2.2. --- Review of Block Based Pattern Matching --- p.2-3Chapter 2.2.1 --- Gradient Descent Strategy --- p.2-3Chapter 2.2.2 --- Simplified Matching Operations --- p.2-10Chapter 2.2.3 --- Fast Full-Search Methods --- p.2-14Chapter 2.2.4 --- Transform-domain Manipulations --- p.2-19Chapter Chapter 3. --- Statistical Rejection Threshold for Pattern Matching --- p.3-1Chapter 3.1. --- Introduction --- p.3-1Chapter 3.2. --- Walsh Hadamard Transform --- p.3-3Chapter 3.3. --- Coarse-to-fine Pattern Matching in Walsh Hadamard Domain --- p.3-4Chapter 3.3.1. --- Bounding Euclidean Distance in Walsh Hadamard Domain --- p.3-5Chapter 3.3.2. --- Fast Projection Scheme --- p.3-9Chapter 3.3.3. --- Using the Projection Scheme for Pattern Matching --- p.3-17Chapter 3.4. --- Statistical Rejection Threshold --- p.3-18Chapter 3.5. --- Experimental Results --- p.3-22Chapter 3.6. --- Conclusions --- p.3-29Chapter 3.7. --- Notes on Publication --- p.3-30Chapter Chapter 4. --- Fast Walsh Search --- p.4-1Chapter 4.1. --- Introduction --- p.4-1Chapter 4.2. --- Approximating Sum-of-absolute Difference Using PS AD --- p.4-3Chapter 4.3. --- Two-level Threshold Scheme --- p.4-6Chapter 4.4. --- Block Matching Using SADDCC --- p.4-10Chapter 4.5. --- Optimization of Threshold and Number of Coefficients in PSAD --- p.4-15Chapter 4.6. --- Candidate Elimination by the Mean of PSAD --- p.4-23Chapter 4.7. --- Computation Requirement --- p.4-28Chapter 4.8. --- Experimental Results --- p.4-32Chapter 4.9. --- Conclusions --- p.4-45Chapter 4.10. --- Notes on Publications --- p.4-46Chapter Chapter 5. --- Conclusions & Future Works --- p.5-1Chapter 5.1. --- Contributions and Conclusions --- p.5-1Chapter 5.1.1. --- Statistical Rejection Threshold for Pattern Matching --- p.5-2Chapter 5.1.2. --- Fast Walsh Search --- p.5-3Chapter 5.2. --- Future Works --- p.5-4References --- p.