Efficient Motion Estimation and Mode Decision Algorithms for Advanced Video Coding

H.264/AVC video compression standard achieved significant improvements in coding efficiency, but the computational complexity of the H.264/AVC encoder is drastically high. The main complexity of encoder comes from variable block size motion estimation (ME) and rate-distortion optimized (RDO) mode decision methods. This dissertation proposes three different methods to reduce computation of motion estimation. Firstly, the computation of each distortion measure is reduced by proposing a novel two step edge based partial distortion search (TS-EPDS) algorithm. In this algorithm, the entire macroblock is divided into different sub-blocks and the calculation order of partial distortion is determined based on the edge strength of the sub-blocks. Secondly, we have developed an early termination algorithm that features an adaptive threshold based on the statistical characteristics of rate-distortion (RD) cost regarding current block and previously processed blocks and modes. Thirdly, this dissertation presents a novel adaptive search area selection method by utilizing the information of the previously computed motion vector differences (MVDs). In H.264/AVC intra coding, DC mode is used to predict regions with no unified direction and the predicted pixel values are same and thus smooth varying regions are not well de-correlated. This dissertation proposes an improved DC prediction (IDCP) mode based on the distance between the predicted and reference pixels. On the other hand, using the nine prediction modes in intra 4x4 and 8x8 block units needs a lot of overhead bits. In order to reduce the number of overhead bits, an intra mode bit rate reduction method is suggested. This dissertation also proposes an enhanced algorithm to estimate the most probable mode (MPM) of each block. The MPM is derived from the prediction mode direction of neighboring blocks which have different weights according to their positions. This dissertation also suggests a fast enhanced cost function for mode decision of intra encoder. The enhanced cost function uses sum of absolute Hadamard-transformed differences (SATD) and mean absolute deviation of the residual block to estimate distortion part of the cost function. A threshold based large coefficients count is also used for estimating the bit-rate part

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

Spatial Prediction in the H.264/AVC FRExt Coder and its Optimization

The chapter presents a review of the fast spatial prediction strategy that were designed for the Intra coding mode of the video coding standard H.264/AVC. At the end, the author presents an effective strategy based on belief propagation message passing

Mode decision for the H.264/AVC video coding standard

H.264/AVC video coding standard gives us a very promising future for the field of video broadcasting and communication because of its high coding efficiency compared with other older video coding standards. However, high coding efficiency also carries high computational complexity. Fast motion estimation and fast mode decision are two very useful techniques which can significantly reduce computational complexity. This thesis focuses on the field of fast mode decision. The goal of this thesis is that for very similar RD performance compared with H.264/AVC video coding standard, we aim to find new fast mode decision techniques which can afford significant time savings. [Continues.

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

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 Intra-frame Coding Algorithm for HEVC Based on TCM and Machine Learning

High Efficiency Video Coding (HEVC) is the latest video coding standard. Compared with the previous standard H.264/AVC, it can reduce the bit-rate by around 50% while maintaining the same perceptual quality. This performance gain on compression is achieved mainly by supporting larger Coding Unit (CU) size and more prediction modes. However, since the encoder needs to traverse all possible choices to mine out the best way of encoding data, this large flexibility on block size and prediction modes has caused a tremendous increase in encoding time. In HEVC, intra-frame coding is an important basis, and it is widely used in all configurations. Therefore, fast algorithms are always required to alleviate the computational complexity of HEVC intra-frame coding. In this thesis, a fast intra-frame coding algorithm based on machine learning is proposed to predict CU decisions. Hence the computational complexity can be significantly reduced with negligible loss in the coding efficiency. Machine learning models like Bayes decision, Support Vector Machine (SVM) are used as decision makers while the Laplacian Transparent Composite Model (LPTCM) is selected as a feature extraction tool. In the main version of the proposed algorithm, a set of features named with Summation of Binarized Outlier Coefficients (SBOC) is extracted to train SVM models. An online training structure and a performance control method are introduced to enhance the robustness of decision makers. When applied on All Intra Main (AIM) full test and compared with HM 16.3, the main version of the proposed algorithm can achieve, on average, 48% time reduction with 0.78% BD-rate increase. Through adjusting parameter settings, the algorithm can change the trade-off between encoding time and coding efficiency, which can generate a performance curve to meet different requirements. By testing different methods on the same machine, the performance of proposed method has outperformed all CU decision based HEVC fast intra-frame algorithms in the benchmarks

Fast Intra-frame Coding Algorithm for HEVC Based on TCM and Machine Learning

High Efficiency Video Coding (HEVC) is the latest video coding standard. Compared with the previous standard H.264/AVC, it can reduce the bit-rate by around 50% while maintaining the same perceptual quality. This performance gain on compression is achieved mainly by supporting larger Coding Unit (CU) size and more prediction modes. However, since the encoder needs to traverse all possible choices to mine out the best way of encoding data, this large flexibility on block size and prediction modes has caused a tremendous increase in encoding time. In HEVC, intra-frame coding is an important basis, and it is widely used in all configurations. Therefore, fast algorithms are always required to alleviate the computational complexity of HEVC intra-frame coding. In this thesis, a fast intra-frame coding algorithm based on machine learning is proposed to predict CU decisions. Hence the computational complexity can be significantly reduced with negligible loss in the coding efficiency. Machine learning models like Bayes decision, Support Vector Machine (SVM) are used as decision makers while the Laplacian Transparent Composite Model (LPTCM) is selected as a feature extraction tool. In the main version of the proposed algorithm, a set of features named with Summation of Binarized Outlier Coefficients (SBOC) is extracted to train SVM models. An online training structure and a performance control method are introduced to enhance the robustness of decision makers. When applied on All Intra Main (AIM) full test and compared with HM 16.3, the main version of the proposed algorithm can achieve, on average, 48% time reduction with 0.78% BD-rate increase. Through adjusting parameter settings, the algorithm can change the trade-off between encoding time and coding efficiency, which can generate a performance curve to meet different requirements. By testing different methods on the same machine, the performance of proposed method has outperformed all CU decision based HEVC fast intra-frame algorithms in the benchmarks

Fast Algorithms for HEVC Rate-Distortion Optimization

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이혁재.디지털 영상 기기의 발전과 더불어, 고화질 영상에 대한 수요 또한 함께 증가하고 있다. 최근의 스마트폰과 태블릿 PC의 급속적인 성장은 이러한 추세를 가속화 시키고 있다. 이러한 변화에 맞추어, 고화질 영상 압축을 위한 새로운 영상 압축 기술의 표준화가 ISO/IEC MPEG과 ITU-T/VCEG의 공동의 팀으로 진행되어 왔다. HEVC는 H.264/AVC의 뒤를 잇는 차세대 영상 압축 표준 기술로서, 2013년 1월 FDIS (Final Draft International Standard)가 작성되면서, 표준화 과정이 완료되었다. HEVC는 H.264/AVC 대비 같은 화질의 영상을 절반의 비트량으로 압축하는 것을 목표로 하였으며, 이런 목표를 달성하기 위해, 새로운 기술들이 제안되었다. 특히, 복잡한 block 구조와 크게 늘어난 mode의 수는 영상 압축의 효율을 향상시키는 데에 크게 기여를 하였고, 이는 최적의 mode를 결정하는 RDO (Rate-Distortion Optimization)가 더욱 중요한 역할을 하도록 만들었다. 그러나, 복잡해진 block 구조는 RDO의 연산량 또한 크게 증가시켰다. 이러한 이유로, H.264/AVC와 달리 HEVC에서는 RDO의 연산량을 줄이면서 압축 효율을 유지하는 것이 중요한 이슈가 되었다. 본 논문에서는, H.264/AVC와 HEVC에서의 RDO에 의한 RD 저하의 차이를 실험 결과를 통해 제시하여 문제를 정의하고, RDO의 연산량을 줄이는 알고리즘들을 세 가지 연구 방향을 통해 제안하였다. 첫 번째 방향의 연구에서는 RDO의 과정을 구성하는 Transform, Quantization, Inverse Quantization, Inverse Transform 그리고 Entropy Coder 등의 일련의 과정의 연산을 단순화하는 알고리즘들이 제안되었다. 이러한 알고리즘은 기본적으로 H.264/AVC에서 이루어진 연구를 기반으로 하였고, 기존 알고리즘의 한계 또한 분석되어 성능을 향상시켰다. 더 나아가서는, 좀 더 공격적으로 RDO의 연산량을 줄일 수 있는 새로운 방법을 제안하였다. 두 번째 방향의 연구에서는 Zero Block detection이라는 기술을 기반으로, HEVC에 적합하게 RDO의 연산을 줄이는 방법을 제안하였다. H.264/AVC에서 제안되었던 알고리즘들은 HEVC에서의 Zero Block을 특징을 제대로 반영하지 못하기 때문에, 단순 수정을 통해 HEVC에 적용할 경우 기대한 만큼의 성능을 얻을 수 없다. 이러한 한계점을 해결하여 HEVC에 적합한 효율적인 Zero Block detection 알고리즘이 제시되었다. 세 번째 방향의 연구에서는, SATD 기반의 RDO를 활용하여, SSE 기반의 RDO의 연산량을 줄이는 방법을 제안하였다. SATD 기반의 RDO와 SSE 기반의 RDO의 차이점 분석과 실험 결과 바탕으로 효율적으로 SATD 기반의 RDO을 활용하는 방법이 제시되었다. 이렇게 제안된 알고리즘들은 HEVC의 reference software인 HM에 구현되어, RDO의 연산량을 크게 줄이면서도, RD 저하가 크게 증가하지 않는 실험 결과를 보이고 있다.초록 iii 목차 v 표 목차 viii 그림 목차 x 제 1 장 서론 1 1.1 연구 배경 1 1.2 연구 내용 3 1.3 논문 구성 6 제 2 장 배경지식과 이전 연구 7 2.1 배경지식 7 2.2 이전 연구 15 제 3 장 Simplified RDO 19 3.1 Simplified SSE 19 3.2 Simplified CABAC 24 3.2.1 CABAC의 구조 24 3.2.2 Various Complexity CABAC 25 3.2.2.1 High-Complexity CABAC 25 3.2.2.2 Medium-Complexity CABAC 26 3.2.2.3 Low-Complexity CABAC 22 3.2.2.4 Evaluation of Various Complexity CABAC 29 3.2.3 Low-Complexity CABAC for HEVC 30 3.3 Advanced Simplified SSE & CABAC 37 3.3.1 Threshold Algorithm 37 3.3.2 Simplified SSE & CABAC without Transform 41 3.4 Evaluation 48 제 4 장 Zero Block Detection 51 4.1 Extension of H.264/AVC Zero Block Detection for HEVC 51 4.1.1 Characteristics of the zero blocks in HEVC 51 4.1.2 ZB detection by an extension of the H.264/AVC algorithm 54 4.2 Zreo Block Detection for HEVC 59 4.2.1 GZB Detection for 16x16 and 32x32 transforms 59 4.2.2 Relaxed conditions for PZB detection 62 4.2.3 Further complexity reduction with SAD(or SATD) test 65 4.2.4 Proposed ZB detection for HEVC 72 4.3 Evaluation 74 제 5 장 SATD based RDO EVALUATION 84 5.1 Difference between SSE based RDO and SATD based RDO 84 5.2 SATD based RDO Evaluation for HEVC 88 5.3 Evaluation 93 제 6 장 결론 95Docto