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Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter in-loop filtering

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1870-1883页

作者： Li, Mingxuan Ji, Wen Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China LonganPi Intelligent Informat Technol Co Ltd Beijing 100080 Peoples R China

Many studies have focused on utilizing convolutional neural networks (CNNs) to enhance loop filter performance in video encoding. However, existing methods primarily concentrate on improving the natural sequence quality rather than addressing the specific needs of screen content sequences, which have gained increased attention due to the growing demands of remote desktops and online meetings. This paper proposed to understand machine behavior from the machine's point of view, and adopts the machine intelligence to screen content coding. It presents a novel loop filter specifically tailored for screen content coding (SCC), referred to as video coding-SCC (VC-SCC). It employs a multiscale feature extraction structure and introduces two innovative non-local models to address distortions in different frame types across various coding setups. Specifically, considering regions of text and graphic textures in screen content, three types of prior maps, including screen content maps, coding configuration maps, and traditional filtering maps, are designed as auxiliary information in the model, promoting distortion pattern learning under different configurations. Two novel non-local models are proposed to enhance the model's ability to capture global features in intra- and inter-frames while keeping low computational complexity. Finally, the VC-SCC is proposed for parallel implementation with the standard in-loop filter, and the optimal results are selected in each patch. Experimental results demonstrate significant performance improvements, with average BD-rate savings of 9.93%, 11.05%, and 10.73% for the all-intra(AI), low-delay(LD), and random-access(RA) configurations, respectively, outperforming other state-of-the-art approaches.

关键词： Encoding Feature extraction Computational modeling Adaptation models Visualization Nonlinear distortion Information filters Deep learning Standards Image color analysis Video coding in-loop filtering screen content deep learning high-efficiency video coding (HEVC)

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in-loop filtering via Trained Look-Up Tables

In-Loop Filtering via Trained Look-Up Tables

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2024 Conference on Visual Communications and Image Processing

作者： Li, Zhuoyuan Li, Jiacheng Li, Yao Li, Li Liu, Dong Wu, Feng Univ Sci & Technol China USTC Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9798331529543;9798331529550

in-loop filtering (ILF) is a key technology in image/video coding for reducing the artifacts. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards, establishing themselves a promising candidate tool for future standards. However, the utilization of deep neural networks (DNN) brings high computational complexity and raises high demand of dedicated hardware, which is challenging to apply into general use. To address this limitation, we study an efficient in-loop filtering scheme by adopting look-up tables (LUTs). After training a DNN with a predefined reference range for in-loop filtering, we cache the output values of the DNN into a LUT via traversing all possible inputs. In the coding process, the filtered pixel is generated by locating the input pixels (to-be-filtered pixel and reference pixels) and interpolating between the cached values. To further enable larger reference range within the limited LUT storage, we introduce an enhanced indexing mechanism in the filtering process, and a clipping/finetuning mechanism in the training. The proposed method is implemented into the Versatile Video Coding (VVC) reference software, VTM-11.0. Experimental results show that the proposed method, with three different configurations, achieves on average 0.13%similar to 0.51%, and 0.10%similar to 0.39% BD-rate reduction under the all-intra (AI) and random-access (RA) configurations respectively. The proposed method incurs only 1%similar to 8% time increase, an additional computation of 0.13 similar to 0.93 kMAC/pixel, and 164 similar to 1148 KB storage cost for a single model. Our method has explored a new and more practical approach for neural network-based ILF.

关键词： in-loop filtering deep neural network Look-up Table (LUT) video coding VVC

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Programmable Low-Power Multicore Coprocessor Architecture for HEVC/H.265 in-loop filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2015年第7期25卷 1217-1230页

作者： Hautala, Ilkka Boutellier, Jani Hannuksela, Jari Silven, Olli Univ Oulu Dept Comp Sci & Engn Ctr Machine Vis Res Oulu 90014 Finland

The High Efficiency Video Coding (HEVC) in-loop filtering is designed to reduce coding artifacts caused by image transforms and quantizations. HEVC in-loop filtering is divided to the deblocking filter and the sample adaptive offset filter, and these two filters take about 20% of total decoding time. This paper presents a very low-power (39 mW) programmable coprocessor architecture to HEVC in-loop filtering, targeting especially embedded devices. The solution consists of three identical tiny application specific instruction set processor cores that are able to process 30 Full-HD intra-luma frames/s when the operating frequency is 350 MHz. The cores are fully programmable by C-language, which allows easy software modifications and updates. Although the cores have been designed for in-loop filtering, they are also capable of signal processing tasks that demand high performance. In terms of energy efficiency, the proposed architecture falls clearly between application-specified integrated circuits and conventional embedded processors, and thus forms a new-generation solution for HEVC in-loop filtering.

关键词： High Efficiency Video Coding (HEVC) in-loop filtering multiprocessor architectures video processing systems

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MFRNet: A New CNN Architecture for Post-Processing and in-loop filtering

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021年第2期15卷 378-387页

作者： Ma, Di Zhang, Fan Bull, David R. Univ Bristol Dept Elect & Elect Engn Bristol BS8 1UB Avon England

In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This network consists of four multi-level feature review residual dense blocks (MFRBs), which are connected using a cascading structure. Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure. In order to further improve information flow between these blocks, each of them also reuses high dimensional features from the previous MFRB. This network has been integrated into PP and ILF coding modules for both HEVC (HM 16.20) and VVC (VTM 7.0), and fully evaluated under the JVET Common Test Conditions using the Random Access configuration. The experimental results show significant and consistent coding gains over both anchor codecs (HEVC HM and VVC VTM) and also over other existing CNN-based PP/ILF approaches based on Bjontegaard Delta measurements using both PSNR and VMAF for quality assessment. When MFRNet is integrated into HM 16.20, gains up to 16.0% (BD-rate VMAF) are demonstrated for ILF, and up to 21.0% (BD-rate VMAF) for PP. The respective gains for VTM 7.0 are up to 5.1% for ILF and up to 7.1% for PP.

关键词： Encoding Video coding Streaming media Standards Video compression Training Feature extraction Deep learning CNN in-loop filtering post-processing video compression HEVC VVC

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Optimal Post-Process/in-loop filtering for improved video compression performance

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2007年第4期53卷 1687-1693页

作者： Kim, Dong-Hwan Oh, Hwa-Yong Urhan, Oguzhan Erturk, Sarp Chang, Tae-Gyu Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea Kocaeli Univ Dept Elect & Telecommun Engn KULIS TR-41040 Kocaeli Turkey

This paper presents a novel optimal filtering approach that can be integrated into video compression schemes to improve video compression performance. In the proposed approach the encoder computes the coefficients of a linear filter in an optimal way, so as to minimize the squared error between the original frame and the filtered reconstructed frame. The encoder then multiplexes the filter coefficients into the bit-stream for decoder access. Reconstructed image frames are filtered at the decoder using optimal filter coefficients to obtain improved image frames. The optimal filtering approach is evaluated in the form of a post-process filter as well as in the form of an in-loop filter. It is shown using H.263+ and H.264/AVC that the proposed approach improves video compression performance of standard compression schemes.

关键词： video compression optimal filtering in-loop filtering post filtering

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Efficient in-loop filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 5352-5366页

作者： Pan, Zhaoqing Yi, Xiaokai Zhang, Yun Jeon, Byeungwoo Kwong, Sam Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Collaborat Innovat Ctr Atmospher Environm Engn Res Ctr Digital ForensMinist Educ Nanjing 210044 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Sungkyunkwan Univ Sch Elect & Elect Engn Suwon 440746 South Korea City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

The raw video data can be compressed much by the latest video coding standard, high efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will incur lots of artifacts in compressed videos, the video quality will be severely influenced. To settle this problem, the in-loop filtering is used in HEVC to eliminate artifacts. Inspired by the success of deep learning, we propose an efficient in-loop filtering algorithm based on the enhanced deep convolutional neural networks (EDCNN) for significantly improving the performance of in-loop filtering in HEVC. Firstly, the problems of traditional convolutional neural networks models, including the normalization method, network learning ability, and loss function, are analyzed. Then, based on the statistical analyses, the EDCNN is proposed for efficiently eliminating the artifacts, which adopts three solutions, including a weighted normalization method, a feature information fusion block, and a precise loss function. Finally, the PSNR enhancement, PSNR smoothness, RD performance, subjective test, and computational complexity/GPU memory consumption are employed as the evaluation criteria, and experimental results show that when compared with the filter in HM16.9, the proposed in-loop filtering algorithm achieves an average of 6.45% BDBR reduction and 0.238 dB BDPSNR gains.

关键词： Image coding Image reconstruction Convolutional neural networks Video recording Quality assessment Video coding Training Convolutional neural networks high efficiency video coding in-loop filtering

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Efficient in-loop filtering Across Tile Boundaries for Multi-Core HEVC Hardware Decoders With 4 K/8 K-UHD Video Applications

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IEEE TRANSACTIONS ON MULTIMEDIA 2015年第6期17卷 778-791页

作者： Cho, Seunghyun Kim, HyunMi Kim, Hui Yong Kim, Munchurl Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305731 South Korea ETRI SoC Res Dept Taejon 305700 South Korea ETRI Realist Broadcasting Media Res Dept Taejon 305700 South Korea

HEVC is a next generation video coding standard designed with modern coding techniques to be especially efficient for coding high-resolution video such as 4 K/8 K-ultra high-definition (UHD) video. Among the advanced coding tools of HEVC, tiles and wavefront parallel processing (WPP) have been newly adopted for parallel processing of such high-resolution (4 K/8 K-UHD) video. To realize UHD video services over portable devices with limited battery power, it is essential to implement multi-core-based and dedicated HEVC hardware decoders that support the tile-and wavefront-based parallel processing. By doing so, each frame is divided into a multiple number of picture partitions which can then be processed by multiple hardware decoder cores in parallel. However, in-loop filtering (ILF) at tile boundaries cannot be easily parallelized by a multi-core HEVC hardware decoder because of the data dependency between samples in different tiles. In this paper, an efficient control method for ILF across tile boundaries is proposed for multi-core HEVC hardware decoders. The proposed method does not require additional in-loop filters for ILF across the tile boundaries and it allows a decoder core to continue to process the next coding tree unit (CTU) without waiting for other decoders until they finish their ILF processing for the neighboring CTUs in other tiles. From experiments, we show the effectiveness of our ILF control method via a quad-core HEVC decoder for 4 K-UHD video implemented on a prototyping FPGA board.

关键词： Hardware decoders HEVC in-loop filtering parallel processing tile

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Recursive Residual Convolutional Neural Network- Based in-loop filtering for Intra Frames

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第7期30卷 1888-1900页

作者： Zhang, Shufang Fan, Zenghui Ling, Nam Jiang, Minqiang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original noncompressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.

关键词： Encoding Video coding Image reconstruction Low-pass filters Adaptive filters Distortion Convolutional neural network in-loop filtering HEVC video coding visual communications

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A novel in-loop filtering mechanism of HEVC based on 3D sub-bands and CNN processing

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SIGNAL IMAGE AND VIDEO PROCESSING 2019年第6期13卷 1045-1053页

作者： Zhang, Dacheng Lei, Weimin Zhang, Wei Chen, Xinyi Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

H.265/HEVC (high efficiency video coding) adopts in-loop filters to reduce artifacts such as blocking artifacts, ringing artifacts, but those above-mentioned artifacts are still obvious in the case of large quantization step. This paper presents a novel in-loop filtering mechanism based on three-dimensional (3D) sub-bands in adaptive group of frames and convolutional neural network (CNN) models for further improvement of in-loop filtering capability. Firstly, the video frame sequence is adaptively grouped;then, a group of video frames is decomposed into frequency sub-bands by 3D wavelet transform;afterward, frequency sub-bands with different types are filtered by corresponding CNN model;finally, the group of video frames is synthesized by 3D wavelet inverse transform. We apply the nonlinear mapping ability of CNN into the modification of wavelet coefficients. The sub-bands of high frequency and low frequency are filtered by four offline CNN models with different directional characteristics trained with training data generated by 3D wavelet transform, respectively. The details of the high frequency part are enhanced, while the quality of low-pass image is promoted, so that those artifacts can be effectively alleviated. By means of comparing experiments, the subjective and objective results show that our proposed method has better filtering performance than the in-loop filtering mechanism in HM16.18;especially the video frame quality can be effectively improved in the case of large quantization step.

关键词： H.265/HEVC in-loop filtering Adaptive group of frames 3D wavelet transform CNN

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Residual Highway Convolutional Neural Networks for in-loop filtering in HEVC

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第8期27卷 3827-3841页

作者： Zhang, Yongbing Shen, Tao Ji, Xiangyang Zhang, Yun Xiong, Ruiqin Dai, Qionghai Tsinghua Univ Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Dept Automat TNLIST Beijing 100084 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Peking Univ Inst Digital Media Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

关键词： Convolutional neural network HEVC in-loop filtering residual highway unit

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