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An Efficient Neural Network Based Rate Control for Intra-Frame in versatile video coding

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6834-6838页

作者： Zhao, Zeming He, Xiaohai Xiong, Shuhua Bi, Xiaodong Chen, Honggang Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

As the prevalence of video continues to grow, the coding, transmission, and processing of video signals is becoming increasingly crucial for intelligent systems, particularly in vehicle driving systems. Moreover, with the rapid advancement of neural networks in the realm of image coding, they emerge as a compelling force shaping the future of video coding. To explore this potential, this paper proposes an Efficient Neural Network Based Rate Control (ENNRC) for intra-frame in versatile video coding (VVC). In particular, a neural network based bits prediction model is developed to directly map video content features to predicted bits, serving as guidance for bit allocation at both the frame and coding Tree Unit (CTU)-levels. Subsequently, we introduce the improved parameter updating algorithm at frame-level. Our experimental findings demonstrate that the proposed RC algorithm achieves a 7.23% BD-rate savings while offering a more accurate allocation compared to VVC's default RC algorithm.

关键词： Neural networks Encoding Prediction algorithms Bit rate Training Feature extraction Accuracy Resource management Reinforcement learning Predictive models Bit allocation neural network rate control versatile video coding

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versatile video coding-Based coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

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IEEE ACCESS 2023年 11卷 34498-34509页

作者： Kim, Shin Lee, Yegi Yoon, Kyoungro Konkuk Univ Dept Comp Sci & Engn Seoul 05029 South Korea

Image analysis based on machine vision is hugely manipulated in the smart industry. Good-quality images are required for outstanding machine analysis results, but handling high-definition images could be problematic in a constrained environment such as a low-bandwidth network or low-capacity storage. Lowering the image resolution might be a straightforward solution for reducing image data, but it would cause much information loss, leading to the deterioration of machine vision. Moreover, human supervision could be necessary for a contingency that machine vision cannot control. Therefore, an innovative image compression method considering machine and human vision is required;more compression efficiency than the state-of-the-art codec, praiseworthy machine vision performance, and human-recognizable quality. In this paper, we propose versatile video coding(VVC) based image compression for hybrid vision, i.e., machine vision and human vision. Our work provides a coding tree unit(CTU) level image compression with dual quantization parameters (QPs) according to the quantization parameter map and the saliency extracted by the object detection network;in the salient region, the proposed method maintains high quality with low QP but degrades the quality with high QP in the non-salient region. Compared with VVC, the proposed compression method achieves a bitrate reduction of up to 25.5% in machine vision tasks, proving more compression efficiency and still admirable machine vision performance. From the perspective of human vision, the proposed method provides human-perceptible image quality, preserving acceptable objective quality values.

关键词： Image coding Machine vision Codecs Bit rate Image reconstruction video coding Object segmentation video coding for machines machine vision hybrid vision versatile video coding

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versatile video coding Based Quality Scalability With Joint Layer Reference

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 2079-2083页

作者： Xiem HoangVan Sang NguyenQuang Pereira, Fernando VNU Univ Engn & Technol Fac Elect & Telecommun Hanoi 10000 Vietnam Inst Super Tecn P-1049001 Lisbon Portugal Inst Telecomunicacoes P-1049001 Lisbon Portugal

Scalability is an essential coding feature for adaptive video streaming applications, notably considering the growing heterogeneity of the transmission, display and consumption environments. versatile video coding (VVC) is the emerging video coding standard, targeting offering higher compression efficiency regarding previous standards to further facilitate already available and novel video applications, notably at higher spatial resolutions. In this context, this letter proposes the first VVC-based quality scalability extension, targeting to offer higher compression efficiency than the native VVC quality scalability solution. The proposed Quality Scalable versatile video coding (QS-VVC) solution is designed based on a layered coding approach with one base layer (BL) and one or more enhancement layers (EL). To achieve higher compression performance, a novel joint layer referencing approach is proposed where the base and enhancement layers decoded information are jointly exploited to create a new EL coding reference. Experimental results shown that the proposed QS-VVC codec outperforms the most relevant benchmarks, notably VVC-based simulcasting, native VVC quality scalability, and the previous Scalable High Efficiency video coding (SHVC) standard.

关键词： Encoding Scalability Standards Streaming media Feature extraction High efficiency video coding Tools versatile video coding quality scalability joint layer reference

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Multiple Quantization Parameter Optimization in versatile video coding for 360° videos

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2020年第3期66卷 213-222页

作者： Adhuran, Jayasingam Kulupana, Gosala Galkandage, Chathura Fernando, Anil Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Consumer Electronics (CE) devices have started to support 360 degrees videos in order to improve the perceived video quality by consumers. Higher compression in 360 degrees videos not only lead to improved consumer perception, but also efficient energy and storage usage in CE devices. Consequently, efficient compression of such video content is becoming a critical design consideration of CE devices. To this end, this paper first proposes a residual weighting algorithm that utilizes the features of Weighted Craster Parabolic Projection Peak Signal-to-Noise Ratio (WCPPPSNR), to instigate the first stage of magnitude reduction of the residuals. The model parameters associated with the residual weighting algorithm are determined in the initial experimental stage. Next, the paper proposes a Quantization Parameter (QP) optimization technique which introduces the second stage of magnitude reduction for residuals with minimal impact on the overall video quality. The proposed method is tested on the versatile video coding (VVC) test model with All-Intra configuration. In this context, the empirical results demonstrate that the proposed method improves the coding efficiency by 3.34% on average and outperforms the state-of-the-art techniques.

关键词： 360 degrees video coding residual weighting adaptive quantization versatile video coding

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High Efficiency Rate Control for versatile video coding Based on Composite Cauchy Distribution

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR video TECHNOLOGY 2022年第4期32卷 2371-2384页

作者： Mao, Yunhao Wang, Meng Wang, Shiqi Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In this work, we propose a novel rate control algorithm for versatile video coding (VVC) standard based on its distinct rate-distortion characteristics. By modelling the transform coefficients with the composite Cauchy distribution, higher accuracy compared with traditional distributions has been achieved. Based on the transform coefficient modelling, the theoretically derived R-Q and D-Q models which have been shown to deliver higher accuracy in characterizing RD characteristics for sequences with different content are incorporated into the rate control process. Furthermore, to establish an adaptive bit allocation scheme, the dependency between different levels of frames is modelled by a dependency factor to describe relationship between the reference and to-be-coded frames. Given the derived R-Q and D-Q relationships, as well as the dependency factor, an adaptive bit allocation scheme is developed for optimal bits allocation. We implement the proposed algorithm on VVC Test Model (VTM) 3.0. Experiments show that due to proper bit allocation, for low delay configuration the proposed algorithm can achieve 1.03% BD-Rate saving compared with the default rate control algorithm and 2.96% BD-Rate saving compared with fixed-QP scheme. Moreover, 1.29% BD-Rate saving and higher control accuracy have also been observed under the random access configuration.

关键词： Encoding Quantization (signal) Bit rate Distortion Transforms Adaptation models video coding versatile video coding rate control rate model distortion model

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Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/versatile video coding

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IEEE ACCESS 2022年 10卷 42127-42136页

作者： Chen, Mei-Juan Lee, Cheng-An Tsai, Yu-Hsiang Yang, Chieh-Ming Yeh, Chia-Hung Kau, Lih-Jen Chang, Chuan-Yu Natl Dong Hwa Univ Dept Elect Engn Hualien 974301 Taiwan Natl Taiwan Normal Univ Dept Elect Engn Taipei 106010 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804201 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 106344 Taiwan Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu 640301 Yunlin Taiwan

H.266/versatile video coding (VVC) is the latest international video coding standard to encode ultra-high-definition video effectively. The quadtree with nested multi-type tree (QT-MTT) structure provides various sizes of coding tree partitioning and allows the nested binary tree (BT) split and ternary tree (TT) split at each QT level. Furthermore, numerous advanced coding tools are equipped in the H.266/VVC encoder. However, the encoding time increases tremendously. Previous researches regarding the fast coding algorithm of H.266/VVC seldom mention perceptual redundancy. This paper utilizes the human vision model of just noticeable difference to extract the visually distinguishable pixels that may affect the visual perception. We observe that the distributions acquired by the horizontal and vertical projections of visually distinguishable pixels within the coding unit are related to their corresponding MTT splitting modes. Therefore, the distributions representing the perceptual information of human vision are used to be the input features of machine learning. Fast MTT decision determined by the random forest models of machine learning is proposed to quickly select the partition for intra coding. Experimental results demonstrate that the proposed method can effectively accelerate intra coding process while maintaining good bitrate and video quality based on the properties of the visual perception. The proposed algorithm provides better performance than the previous work.

关键词： Encoding Random forests video coding Streaming media Copper Visual perception Licenses H266 versatile video coding intra coding visual perception just noticeable difference machine learning random forest

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An explicit self-attention-based multimodality CNN in-loop filter for versatile video coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第29期81卷 42497-42511页

作者： Jia, Menghu Gao, Yanbo Li, Shuai Yue, Jian Ye, Mao Univ Elect Sci & Technol China Chengdu Peoples R China Shandong Univ Shandong Peoples R China

The newest video coding standard, versatile video coding (VVC), has just been published recently. While it greatly improves the performance over the last High Efficiency video coding (HEVC) standard, there are still blocking artifacts under the more flexible block partitioning structures. In order to reduce the blocking artifact and improve the quality of the reconstructed video frame, an explicit self-attention-based multimodality convolutional neural network (CNN) is proposed in this paper. It adaptively adjusts the restoration of different coding units (CU) according to the CU partition structure and texture of the reconstructed video frame, considering that the loss scales of different CUs can be quite different. The proposed method takes advantage of the CU partition map by using it as a different modality and combined with the attention mechanism. Moreover, the unfiltered reconstructed image is also used to enhance the attention branch, which forms an explicit self-attention model. Then a densely integrated multi-stage fusion is developed where the attention branch is densely fused to the main filtering CNN to adaptively adjust the overall image recovery scale. Thorough analysis on the proposed method is provided with ablation study on each module. Experimental results show that the proposed method achieves the state-of-the-art performance under all intra (AI) configuration, with 7.24% BD-rate savings on average compared with the VVC reference software (VTM).

关键词： versatile video coding VVC in-loop filter CNN attention

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Deep Learning-Based Chroma Prediction for Intra versatile video coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR video TECHNOLOGY 2021年第8期31卷 3168-3181页

作者： Zhu, Linwei Zhang, Yun Wang, Shiqi Kwong, Sam Jin, Xin Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Color images always exhibit a high correlation between luma and chroma components. Cross component linear model (CCLM) has been introduced to exploit such correlation for removing redundancy in the on-going video coding standard, i.e., versatile video coding (VVC). To further improve the coding performance, this paper presents a deep learning based intra chroma prediction method, termed as convolutional neural network based chroma prediction (CNNCP). More specifically, the process of chroma prediction is formulated to produce the colorful version from available information input. CNNCP includes two sub-networks for luma down-sampling and chroma prediction, which are jointly optimized to fully exploit spatial and cross component information. In addition, the outputs of CCLM are adopted as chroma initialization for performance enhancement, and the coding distortion level characterized by quantization parameter is fed into the network to release the negative affect from compression artifacts. To further improve the coding performance, the competition is performed between the conventional chroma prediction and CNNCP in terms of rate-distortion cost with a binary flag signalled. The learned CNNCP is incorporated into both video encoder and decoder. Extensive experimental results demonstrate that the proposed scheme can achieve 4.283%, 3.343%, and 4.634% bit rate savings for luma and two chroma components, compared with the VVC test model version 4.0 (VTM 4.0).

关键词： video coding Encoding Image color analysis Redundancy Transforms Motion compensation Neural networks Deep learning convolutional neural network chroma prediction versatile video coding

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Complexity assessment of the intra prediction in versatile video coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第18期82卷 27751-27770页

作者： Zouidi, Naima Kessentini, Amina Hamidouche, Wassim Masmoudi, Nouri Menard, Daniel ENI Sfax LETI 20 Ave Buttes Coesmes F-35708 Rennes 7 France INSA Rennes IETR 20 Ave Buttes Coesmes F-35708 Rennes 7 France ENI Sfax LETI Rd Soukra Sfax 3038 Tunisia ISIM Gabes Rd Soukra Sfax 3038 Tunisia

The versatile video coding (VVC) was finalized in July 2020 by the Joint video Experts Team (JVET). During the standardization process, many coding techniques were investigated in order to provide superior coding efficiency with high level of versatility over the High Efficiency video coding (HEVC). Indeed, VVC achieved about 40% of bitrate saving compared to its predecessor for the same objective visual quality. However, its encoding complexity has considerably increased due to the exhaustive Rate-Distortion (RD) search. In this paper, a complexity assessment of the intra mode decision of VVC is presented. Experimental results reveal that the intra coding tools and the steps of intra mode decision have various degrees of complexity. Thus, opening up several complexity reduction opportunities, that yield up to 43% of complexity reduction. Also, VVC tends usually to select the 6 Most Probable Modes (MPMs). However, this selection may depend on the used intra coding tool and video texture characteristics. These conclusions give clear openings to accelerate the intra mode decision.

关键词： versatile video coding Complexity reduction Intra prediction Rate distortion Intra mode decision

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A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第12期81卷 17205-17222页

作者： Yao, Yingbiao Wang, Jiaojiao Du, Chenjie Zhu, Jinghui Xu, Xin Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

versatile video coding (VVC/H.266) expands the intra-frame prediction modes from 35 of H.265 to 67, which not only improves the prediction accuracy and coding efficiency of the encoder but also increases its encoding complexity. In response to this problem, this paper proposes a support vector machine-based fast planar prediction mode decision algorithm for VVC/H.266, which is used to quickly determine the Planar or non-Planar prediction modes during intra coding, avoiding the rate-distortion optimization calculation of multiple intra prediction modes, and thus reducing the encoding time. The contributions of this paper are: 1) It proposes a novel feature based on Statistical Oriented Gradient (SOG) to extract the feature information of the coding block;2) Based on the SOG feature, a support vector machine-based method is proposed to make the decision between the Planar and non-Planar modes for intra coding of VVC/H.266. The proposed algorithm has been integrated into the VVC/H.266 standard coder, VTM5.0. The experimental results show that, compared with VTM5.0, the encoding time of the proposed algorithm is reduced by 18.0% on average, while the Bjontegaard-Delta -Rate (BD-RATE) increased by only 1.3%.

关键词： versatile video coding Support vector machine Mode decision Intra prediction

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