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12th IEEE International Symposium on Signal Processing and Information Technolgy (ISSPIT)

作者： Lokkoju, Satish Reddy, Dinesh Samsung India Software Operat Private Ltd Bangalore Karnataka India

ISBN: (纸本)9781467356046

Quad tree based encoders do brute force search for finding out the best partition for coding unit (CU). This brute force search performs encoding for all the possible block sizes and selects the partition size that gives best compression. This search along with inherent complexity of the latest encoders makes it extremely difficult to attain real time performance of 30 fps and low power. The solution to this problem is to perform a low complexity analysis of the coding unit and suggest the partition of the CU based on the available CU characteristics without performing entire encoding to estimate the cost. The present paper describes a method to do this using Sum of Absolute Difference, hereby SAD, and gradient information of the coding unit. We show that the presented method results in 3x faster encoding when compared to the brute force algorithm with small increase in bitrate (approximately 5% increase in worst case) and no change in subjective quality. The complexity bitrate trade off and the result BD-PSNR values of this method are also presented.

关键词： Quad tree HEVC coding unit partition SAD Mode decision

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A Fast SAO Algorithm Based on coding unit partition for HEVC 6

A Fast SAO Algorithm Based on Coding Unit Partition for HEVC

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6th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhang Zhengyong Chen Zhiyun Pu Peng East China Normal Univ Coll Informat Sci Technol Shanghai Peoples R China

ISBN: (纸本)9781479983537

Sample adaptive offset (SAO) filter algorithm is introduced by HEVC as a completely new stage to improve video quality. It is located after deblocking filter, adding an offset value to each sample based on the reconstructed data and the original YUV. In order to solve the problem of high computational complexity of SAO, a fast SAO algorithm based on coding unit partition is proposed in this paper. Evaluating the texture complexity of coding unit by the depth of block partitioning, and only the complex coding unit performed the SAO filter. Simulation results show that, compared with the HEVC conference software HM9.0, the proposed algorithm can reduce about 44.55% encoding time up to 83.68%, while it suffers from negligible on PSNR performance.

关键词： video coding HEVC standard coding unit partition SAO filter texture characteristic coding efficiency

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A Fast SAO Algorithm Based on coding unit partition for HEVC

A Fast SAO Algorithm Based on Coding Unit Partition for HEVC

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Zhang Zhengyong Chen Zhiyun Pu Peng College of Information Science Technology East China Normal University

Sample adaptive offset（SAO） filter algorithm is introduced by HEVC as a completely new stage to improve video *** is located after deblocking filter,adding an offset value to each sample based on the reconstructed data and the original *** order to solve the problem of high computational complexity of SAO,a fast SAO algorithm based on coding unit partition is proposed in this *** the texture complexity of coding unit by the depth of block partitioning,and only the complex coding unit performed the SAO *** results show that,compared with the HEVC conference software HM9.0,the proposed algorithm can reduce about 44.55%encoding time up to 83.68%,while it suffers from negligible on PSNRperformance.

关键词： video coding HEVC standard coding unit partition SAO filter texture characteristic coding efficiency

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Fast intra partition and mode prediction for equirectangular projection 360-degree video coding

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IET IMAGE PROCESSING 2023年第2期17卷 558-569页

作者： Shu Zheng-Jie Peng Zong-Ju Jiang Gang-Yi Chen Fen Yuan Bo-Sen Ningbo Univ Fac Informat Sci & Engn Ningbo Peoples R China Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China

360-degree videos have drawn great attention from both the academia and the industry. For transmission and storage of 360-degree video, the joint video exploration team proposed the Versatile Video coding (VVC) standard. VVC can significantly reduce the bitrate while maintaining the same subjective visual quality compared to the preceding high efficiency video coding. However, the computational complexity of VVC is extremely high which hinders the interactive applications. This paper proposes a fast intra partition method and mode prediction algorithm for equirectangular projection 360-degree video coding. First, a latitude-based preprocessing is introduced to early terminate the coding unit (CU) partition in the polar region. Second, the support vector machine is used to predict the CU partition type. Third, the fast intra mode search method accelerates the intra mode prediction. Experimental results show that the proposed algorithm can significantly obtain an average time reduction rate of 60.40% and a Bjontegaard delta rate increase of 1.96%.

关键词： VVC preceding high efficiency video coding fast intra mode search method Support vector machines Optimisation techniques mode prediction algorithm search problems Image and video coding joint video exploration team video coding support vector machines intra mode prediction equirectangular projection 360-degree video coding fast intra partition method Versatile Video coding standard Video signal processing coding unit partition computational complexity

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Width-Adaptive CNN: Fast CU partition Prediction for VVC Screen Content coding

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9372-9382页

作者： Jiao, Chao Zeng, Huanqiang Chen, Jing Hsia, Chih-Hsien Wang, Tianlei Ma, Kai-Kuang Huaqiao Univ Sch Informat Sci & Engn Xiamen 361021 Peoples R China Huaqiao Univ Sch Engn Quanzhou 362021 Peoples R China Ilan Univ Dept Comp Sci & Informat Engn Yilan 260 Taiwan Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejiang Hangzhou 310018 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

Screen content coding (SCC) in Versatile Video coding (VVC) improves the coding efficiency of screen content videos (SCVs) significantly but results in high computational complexity due to the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partitioning. Therefore, we make the first attempt to reduce the encoding complexity from the perspective of CU partitioning for SCC in VVC. To this end, a fast CU partition prediction method is technically developed for VVC-SCC. First, to solve the problem of lacking sufficient SCC training data, SCVs are collected to establish a database containing CUs of various sizes and corresponding partition labels. Second, to determine the partition decision in advance, a novel WA-CNN model is proposed, which is capable of predicting two large CUs for VVC-SCC by adjusting the feature channels based on the size of input CU blocks. Finally, considering the imbalanced proportion of diverse partition decisions, a loss function with the weight that equalizes the contribution of imbalanced data is formulated to train the proposed WA-CNN model. Experimental results show that the proposed model reduces the SCC intra-encoding time by 35.65%similar to 38.31% with an average of 1.84%similar to 2.42% BDBR increase.

关键词： Screen content coding (SCC) versatile video coding (VVC) coding unit partition deep learning

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Block-Dependent partition Decision for Fast Intra coding of VVC

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 277-289页

作者： Peng, Zixiao Shen, Liquan Ding, Qing Dong, Xinchao Zheng, Linru Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Beihang Univ Sch Commun & Informat Engn Beijing 100191 Peoples R China Agora Lab Inc Video Algorithm Santa Clara CA 95054 USA

Aiming at accelerating the intra coding process of versatile video coding (VVC), previous efforts are made to predict the quad-tree plus multi-type tree (QTMT) partition structure, in which the prediction is modeled as a classification procedure. The existing deep learning-based methods usually represent the block partition as a structural output and utilize a single convolutional neural network (CNN) for the prediction of all blocks. However, they take different blocks equally, i.e., one-for-all, which ignores that the blocks with different complexity of partition structures are unevenly distributed and have different prediction difficulties. To address this problem, we propose a novel block-dependent partition decision (BDPD) framework to adaptively process different blocks by networks with different capacities. Specifically, we design a partition homogeneity map (PHM) to represent the QTMT-based block partition, which combines different partition directions to effectively reflect the complexity of the partition structure. On this basis, we propose a class-based prediction to distinguish blocks with different complexity of the partition structure and adopt appropriate FCN models to predict PHM, incorporating block classification and PHM prediction. The blocks are classified into different classes according to their coarse texture and neighboring PHMs. Then different fully convolutional network (FCN) models are utilized to predict PHM for different classes. The FCN models with different capacities are trained in the corresponding class, respectively, which achieves higher performance with less computation on the extremely unbalanced natural video. Finally, an adaptive partition decision based on predicted PHMs is adopted to conduct partition decisions for a better trade-off between rate-distortion performance and encoding complexity. Experimental results show that our approach achieves 45.7%similar to 74.5% encoding complexity reduction with 0.78%similar to 3.38% B

关键词： Versatile video coding intra coding coding unit partition quadtree plus multi-type tree complexity reduction deep learning

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Fast Intra partition Algorithm for HEVC Screen Content coding

Fast Intra Partition Algorithm for HEVC Screen Content Codin...

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IEEE Visual Communications and Image Processing Conference

作者： Zhang, Mengmeng Guo, Yuhui Bai, Huihui North China Univ Technol Coll Informat Engn Beijing Peoples R China BeiJing Jiaotong Univ Inst Informat Sci Beijing Peoples R China

ISBN: (纸本)9781479961399

Since the publication of the High Efficiency Video coding standard as the newest video coding standard, several extensions have been made. Among these, the use of the screen content coding in many fields is one of the important extensions. In terms of coding tree unit (CTU) partitioning, rate distortion optimization is still used in screen content coding. The complexity of the process has resulted in problems in relation to real-time application. Thus, this paper proposes a fast-deciding CTU partition mode algorithm based on entropy and coding bits. Experimental results show that the proposed algorithm can save 32% of encoding time on average compared with the default algorithm in HM-12.1+RExt-5.1 with only 0.8% bit rate increment in coding performance.

关键词： HEVC screen content coding unit partition entropy coding bits

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Random Forest Based Fast CU partition for VVC Intra coding 16

Random Forest Based Fast CU Partition for VVC Intra Coding

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16th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： He, Quan Wu, Wenxin Luo, Lei Zhu, Ce Guo, Hongwei Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Honghe Univ Sch Engn Mengzi Peoples R China

ISBN: (纸本)9781665449083

Versatile Video coding (VVC) significantly improves the coding efficiency over the preceding high efficiency video coding (HEVC) standard, but at the expense of much higher computational complexity. Specifically for intra coding of VVC, the computational burden is mainly on the brute-force recursive rate-distortion optimization (RDO) search of quadtree with nested multi-type tree (QTMT) based coding unit (CU) partition structure. Consequently, we propose a random forest based algorithm to reduce the complexity of CU partition. The CUs are first divided into three categories, namely simple, fuzzy, and complex CUs. For simple and complex CUs, one random forest classifier is trained to directly predict the optimal partition mode. For fuzzy CUs, another random forest is trained to predict whether the partition process is terminated or not. The experimental results show that the complexity reduction of the proposed algorithm is up to 69% as compared to the VVC reference software (VTM 7.0), and averagely 57% encoding time saving is achieved with 1.21% BDBR increase.

关键词： Versatile video coding intra coding quadtree with nested multi-type tree coding unit partition random forest

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Fast CU partition Algorithm for Intra Frame coding Based on Joint Texture Classification and CNN

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SENSORS 2023年第18期23卷 7923-7923页

作者： Wang, Ting Wei, Geng Li, Huayu Bui, Thioanh Zeng, Qian Wang, Ruliang Nanning Normal Univ Sch Phys & Elect Nanning 530100 Peoples R China Guangxi Minzu Univ Technol Coll Xiangsihu Sch Nanning 530100 Peoples R China

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.

关键词： texture classification CNN HEVC/H.265 coding unit partition

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DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU partition of Intra-Mode VVC

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 5377-5390页

作者： Li, Tianyi Xu, Mai Tang, Runzhi Chen, Ying Xing, Qunliang Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Alibaba Grp Hangzhou 311121 Peoples R China

Versatile Video coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving a desirable trade-off between complexity and RD performance. The experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%similar to 66.88% with a negligible Bjontegaard delta bit-rate (BD-BR) of 1.322%similar to 3.188%, significantly outperforming other state-of-the-art approaches.

关键词： Versatile video coding complexity reduction coding unit partition deep learning

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