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Neural Network Based Multi-Level in-loop filtering for Versatile Video Coding

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 12092-12096页

作者： Zhu, Linwei Zhang, Yun Li, Na Wu, Wenhui Wang, Shiqi Kwong, Sam Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China

To further improve the performance of Versatile Video Coding (VVC), a neural network based multi-level in-loop filtering framework for luma and chroma is presented in this letter, which includes Reference pixel Level (RL), Coding tree unit Level (CL), and Frame Level (FL). The neural network based filters in these levels can be flexibly enabled. In RL, the coding performance upper bound is analyzed and asymmetric convolution is designed. In CL, the pixels located at the bottom and rightmost have been assigned greater weights for loss calculation during training. In addition, the co-located luma is adopted in CL and FL chroma filtering for guiding chroma enhancement due to the high correlation between them. For the architecture of neural network, two input channel fusion schemes are combined to enjoy both of their benefits. Extensive experimental results show that the proposed multi-level in-loop filtering method can achieve 6.87%, 32.8%, and 36.9% bit rate reductions on average for Y, U, and V components under all intra configuration, which outperforms the state-of-the-art works.

关键词： in-loop filtering versatile video coding neural network multi-level

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A Novel Low Delay in-loop filtering WPP Process for Parallel HEVC Encoding

A Novel Low Delay In-Loop Filtering WPP Process for Parallel...

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30th IEEE Conference on Visual Communications and Image Processing (VCIP)

作者： Gu, Jiawen Han, Yuxing Wen, Jiangtao Tsinghua Univ Dept Comp Sci Beijing 10084 Peoples R China

ISBN: (纸本)9781509053162

Wave front parallel processing (WPP) is a parallelization technique that enables processing of several rows of Largest Coding Units (LCUs) in parallel. It achieves a relatively high level of parallelism with reasonable loss in compression performance. At the same time, the HEVC standard specifies two in-loop filters, namely the deblocking filter and the sample adaptive offset (SAO), to improve subjective quality as well as coding efficiency. Because SAO parameters cannot be precisely determined until the lower right deblocked samples are available, a delay of several rows is introduced to implement the filtering process. Since the reconstruction of the LCUs will not start until finish filtering, the total delay of WPP and filtering is at least two rows, which obviously influences the performance of parallel processing. In this paper, we propose a novel In loop filtering WPP method that reduces the row delay into four LCUs and significantly improves the parallelism with little rate-distortion (RD) performance loss. Experimental results show that the proposed algorithms can achieve up to the 2.89x speedup compared with the existing WPP method with 24-core server, where the speedup improves with the increase of core number.

关键词： in-loop filtering SAO deblocking filter wave-front parallel processing low delay

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ON INTRA VIDEO CODING AND in-loop filtering FOR NEURAL OBJECT DETECTION NETWORKS

ON INTRA VIDEO CODING AND IN-LOOP FILTERING FOR NEURAL OBJEC...

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IEEE International Conference on Image Processing (ICIP)

作者： Fischer, Kristian Herglotz, Christian Kaup, Andre Friedrich Alexander Univ Erlangen Nurberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163956

Classical video coding for satisfying humans as the final user is a widely investigated field of studies for visual content, and common video codecs are all optimized for the human visual system (HVS). But are the assumptions and optimizations also valid when the compressed video stream is analyzed by a machine? To answer this question, we compared the performance of two state-of-the-art neural detection networks when being fed with deteriorated input images coded with HEVC and VVC in an autonomous driving scenario using intra coding. Additionally, the impact of the three VVC in-loop filters when coding images for a neural network is examined. The results are compared using the mean average precision metric to evaluate the object detection performance for the compressed inputs. Throughout these tests, we found that the Bjontegaard Delta Rate savings with respect to PSNR of 22.2 % using VVC instead of HEVC cannot be reached when coding for object detection networks with only 13.6 % in the best case. Besides, it is shown that disabling the VVC in-loop filters SAO and ALF results in bitrate savings of 6.4% compared to the standard VTM at the same mean average precision.

关键词： machine to machine communication video coding for machines (VCM) neural object detection versatile video coding (VVC) in-loop filtering

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ASYMMETRIC CONVOLUTIONAL RESIDUAL NETWORK FOR AV1 INTRA in-loop filtering

ASYMMETRIC CONVOLUTIONAL RESIDUAL NETWORK FOR AV1 INTRA IN-L...

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IEEE International Conference on Image Processing (ICIP)

作者： Xia, Jiangyue Wen, Jiangtao Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728163956

In video compression standards, in-loop filtering plays an important role in alleviating blocking, blurring and ringing artifacts caused by lossy compression, which enhances visual quality and benefits coding efficiency. The boom of neural network applications in super-resolution and image restoration brings insights into solutions of in-loop filtering in video codecs. In this paper, we design an asymmetric convolutional residual network (ACRN) for in-loop filtering in the state-of-the-art AV1 codec. With the asymmetric convolutional blocks, directional features can be extracted to restore textures and improve quality. The cascading structure of wide-activated residual blocks with pruned dense connections enables reflecting hierarchical coding unit (CU) partition characteristics of video coding without losing overall details. Experiments show that the proposed lightweight ACRN can bring up to 12.78% coding efficiency improvement in intra coding of AV1.

关键词： in-loop filtering AV1 residual network asymmetric convolution

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Complexity Reduction of Learned in-loop filtering in Video Coding

Complexity Reduction of Learned In-Loop Filtering in Video C...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Bayliss, Woody Murn, Luka Izquierdo, Ebroul Zhang, Qianni Mrak, Marta Queen Mary Univ London MMV London England British Broadcasting Corp R&D London England

ISBN: (纸本)9781665484855

In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output. Conventional in-loop filters are obtained by hand-crafted methods. Recently, learned filters based on convolutional neural networks that utilize attention mechanisms have been shown to improve upon traditional techniques. However, these solutions are typically significantly more computationally expensive, limiting their potential for practical applications. The proposed method uses a novel combination of sparsity and structured pruning for complexity reduction of learned in-loop filters. This is done through a three-step training process of magnitude-guided weight pruning, insignificant neuron identification and removal, and fine-tuning. Through initial tests we find that network parameters can be significantly reduced with a minimal impact on network performance.

关键词： neural networks video coding in-loop filtering quality enhancement pruning sparsity

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CNN-BASED in-loop filtering FOR CODING EFFICIENCY IMPROVEMENT 12

CNN-BASED IN-LOOP FILTERING FOR CODING EFFICIENCY IMPROVEMEN...

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12th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

作者： Park, Woon-Sung Kim, Munchurl Korea Adv Inst Sci & Technol Dept Elect Engn 291 Daehak Ro Taejon 305701 South Korea

ISBN: (纸本)9781509019298

A recent video coding standard, called High Efficiency Video Coding (HEVC), adopts two in-loop filters for coding efficiency improvement where the in-loop filtering is done by a de-blocking filter (DF) followed by sample adaptive offset (SAO) filtering. The DF helps improve both coding efficiency and subjective quality without signaling any bit to decoder sides while SAO filtering corrects the quantization errors by sending offset values to decoders. In this paper, we first present a new in-loop filtering technique using convolutional neural networks (CNN), called IFCNN, for coding efficiency and subjective visual quality improvement. The IFCNN does not require signaling bits by using the same trained weights in both encoders and decoder. The proposed IFCNN is trained in two different QP ranges: QR1 from QP = 20 to QP = 29;and QR2 from QP = 30 to QP = 39. In testing, the IFCNN trained in QR1 is applied for the encoding/decoding with QP values less than 30 while the IFCNN trained in QR2 is applied for the case of QP values greater than 29. The experiment results show that the proposed IFCNN outperforms the HEVC reference mode (HM) with average 1.9% - 2.8% gain in BD-rate for Low Delay configuration, and average 1.6% - 2.6% gain in BD-rate for Random Access configuration with IDR period 16.

关键词： Convolutional neural network High Efficiency Video Coding sample adaptive offset in-loop filtering

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Quadtree-based Guided CNN for AV1 in-loop filtering 29

Quadtree-based Guided CNN for AV1 In-loop Filtering

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IEEE International Conference on Image Processing (ICIP)

作者： Wang, Junjie Ding, Gongchun Ding, Dandan Mukherjee, Debargha Joshi, Urvang Chen, Yue Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Google Inc Mountain View CA 94043 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recently, learning-based in-loop filtering has attracted lots of attention. State-of-the-art works generally deploy computationally expensive, large-scale neural networks, which is unfriendly to practical applications. Besides, since these models are generally pre-trained using a limited dataset and applied to various videos, they may fail in video contents excluded in the training dataset. To address these issues, this paper develops a Guide CNN in-loop filtering framework to obtain the restored signal. Our basic idea is to construct a subspace and use the projection of the original signal into this subspace to approximate the original signal itself. Specifically, we employ CNN to transform the degraded signal into M subsignals to construct the optimal subspace since the training of CNN is essentially an optimization procedure. Furthermore, unlike existing CNN models that process all blocks uniformly, our method leverages a quadtree structure to implement the Guided CNN through R-D optimization. As such, the best partition to Guided CNN can be determined. We exemplify the proposed method in AV1 codec. Experimental results show that the Guided CNN framework achieves 2.19% and 1.31% BD-Rate gains over the AV1 anchor in intra and inter coding mode, respectively, while the normal CNN achieves only 1.64% and 1.04%.

关键词： Guided CNN in-loop filtering least square optimization inter frame coding

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Comparative Analysis Overview of CNN-Driven in-loop filtering Approaches in H.266/VVC

Comparative Analysis Overview of CNN-Driven In-Loop Filterin...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Hussain, Hanan Tamizharasan, P.S. Bits Pilani Department of Computer Science Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798350372229

Video compression standards such as HEVC/H.265 and VVC/ H.266 have a hybrid coding framework consisting of various modules, including in-loop filters (ILFs), to reduce data size while maintaining video quality. However, the default ILFs in these standards have limitations and may not adapt well to complex natural videos. This review paper explores the use of Convolutional Neural Networks (CNNs) to enhance the performance of ILFs in video compression. CNN-based ILFs are categorized based on their position: replacing default ILFs, placed after existing ILFs, or inserted between default ILFs. They are also categorized by training setups: QP combined and separate models, frame combined and separated models, and channel combined and separated models. The paper provides a comparative analysis of recent CNN-based ILFs regarding their extra inputs, trained channels, Y/BD rate (PSNR), encoding/decoding time, and other metrics, including subjective quality analysis and complexity analysis. The reviewed CNN-based ILFs offer promising results in terms of video compression efficiency and quality enhancement, demonstrating the potential for further advancements in this field. However, there are still challenges to address, including the trade-off between compression efficiency and complexity, the integration of multiple deep tools, and more. This research highlights the importance of CNN-based ILFs in improving video compression standards and their potential for future developments in video coding. © 2024 IEEE.

关键词： CNN-Based Filters H.264 in-loop filtering Video Compression Video Quality Enhancement

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A progressive CNN in-loop filtering approach for inter frame coding

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 94卷 116201-116201页

作者： Ding, Dandan Kong, Lingyi Wang, Wenyu Zhu, Fengqing Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou Peoples R China Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Convolutional Neural Network (CNN) structures have been designed for in-loop filtering to improve video coding performance. These CNN models are usually trained through learning the correlations between the reconstructed and the original frames, which are then applied to every single reconstructed frame to improve the overall video quality. This direct model training and deployment strategy is effective for intra coding since a locally optimal model is sufficient. However, when applied to inter coding, it causes over-filtering because the intertwined reference dependencies across inter frames are not taken into consideration. To address this issue, existing methods usually resort to the Rate?Distortion Optimization (RDO) to selectively apply the CNN model, but fail to address the limitation of using a local CNN model. In this paper, we propose a progressive approach to train and incorporate the CNN-based in-loop filters to work seamlessly with video encoders. First, we develop a progressive training method to obtain the inter model. Using transfer learning, reconstructed frames using the CNN model are progressively involved back into the training of the CNN model itself, to simulate the reference dependencies in inter coding. Next, we design a frame-level model selection strategy for the high-bitrate coding where the over-filtering effect is diluted. Experimental results show that the proposed method outperforms the RDO method that utilizes only local model. Proposed approach also achieves comparable coding performance but with less computational complexity when integrating our progressive model into the RDO scheme.

关键词： CNN in-loop filtering Model training Inter coding

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Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 92卷 116109-116109页

作者： Xu, Qiang Jiang, Xinghao Sun, Tanfeng Kot, Alex C. Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Natl Engn Lab Informat Content Anal Tech GT036001 Shanghai 200240 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 637553 Singapore

With the increasing maturity of video editing technology, forgers are more inclined to transcode videos to High Efficiency Video Coding (HEVC) videos, as HEVC not only enables people to enjoy high-definition videos but also allows broadcasters to stream it more efficiently across networks. Therefore, to verify the originality and authenticity, it is of great significance to propose an algorithm for detecting transcoded HEVC videos. In this paper, a theoretical model of video transcoding is first constructed, and a novel transcoding detection algorithm based on in-loop filtering and Prediction Units (PU) Partition (IFPP) is proposed. By analyzing the statistical characteristics of strong and normal filtering modes in deblocking filtering and calculating offset values in Sample Adaptive Offset (SAO) filtering, the transcoding traces in inter-coded frames can be captured. In addition, PU partition statistics are also extracted to make full use of traces in intra-coded frames. By fusing these subfeatures, the proposed IFPP feature with 17 dimensions can be obtained, which is further fed to the Support Vector Machine (SVM) classifier. Finally, experiments are conducted on datasets with various coding parameters. Results show that the proposed algorithm outperforms existing algorithms and has better robustness.

关键词： Transcoding detection HEVC in-loop filtering Prediction Units Support Vector Machine

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