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14th International Conference on Quality of Multimedia Experience (QoMEX)

作者： Satti, Shahid Mahmood Obermann, Matthias Schmidmer, Christian Keyhl, Michael Optic GmbH Erlangen Germany

ISBN: (数字)9781665487948

ISBN: (纸本)9781665487948

Spatial/temporal information (SI/TI) is used to characterize the scene-complexity of videos. It is often hypothesized that SI/TI can also be used to measure the encoding-complexity of a video;although higher scene-complexity may not always mean a higher encoding-complexity for videos. In this paper, two alternative views on the video encoding-complexity are proposed. In the first view, a ground-truth of encoding-complexity is derived using the area-under-the-curve of a rate-quality plot. In the second view, constant rate-factor (CRF) video encoding is used to synthesize a measure of video encoding-complexity. Experimental evaluation on 734 UHD video sequences showed that the CRF based method, compared to SI/TI, yields better complexity characterization w.r.t. the ground-truth.

关键词： video quality encoding complexity rate distortion

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On the encoding complexity of Quasi-Cyclic LDPC Codes

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第22期63卷 6096-6108页

作者： Mahdi, Ahmed Paliouras, Vassilis Univ Patras Elect & Comp Engn Dept VLSI Design Lab Patras 26500 Greece

In this paper, we propose a parity check matrix (PCM) construction technique that reduces the encoding complexity of quasi-cyclic (QC)-LDPC codes. The proposed construction method is based on a constraint selection of shifting factors, shown here to reduce the density of an inverted matrix used in several encoding algorithms. Furthermore, it demonstrates that the complexity of encoding schemes involving inverted matrices, can be defined by the density of the small inverted binary base matrix and not by the extended QC-PCM. Comparisons of the proposed codes with codes employed in international standards and with randomly shifted QC-LDPC codes of comparable characteristics, show the low complexity of the corresponding hardware implementations and a BER performance equivalent to that of previously reported codes without increasing the decoding complexity. Furthermore, adoption of the proposed method can decrease the complexity of several encoding procedures;in particular, an area reduction of 40%-55% is reported for QC-LDPC encoders, while a reduction of 86% is reported for Multi-Level-QC-LDPC encoders.

关键词： BER performance density reduction encoding complexity hardware architecture LDPC encoding matrix inversion quasi-cyclic LDPC

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Ultra-Lightweight CNN Based Fast Intra Prediction for VVC Screen Content Coding 56

Ultra-Lightweight CNN Based Fast Intra Prediction for VVC Sc...

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tang, Tong You, Chuan Li, Zhidu Zhang, Ruoying Zou, Hong Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Adv Network & Intelligent Interconnect Technol Ke Chongqing Peoples R China Chongqing Key Lab Ubiquitous Sensing & Networking Chongqing Peoples R China

ISBN: (纸本)9781665451093

Versatile Video Coding (VVC/H.266) greatly improves the compression performance at the cost of extremely high computational complexity compared with High Efficiency Video Coding (HEVC/H.265). Within the context of mobile devices with limited power and computational capabilities, reductions on encoding complexity are important;particularly to encode new data formats such as screen content sequences. Therefore, in this paper, aimed at two brand-new techniques with high complexity, matrix-weighted intra-prediction (MIP) and intra-sub-partition (ISP), we propose a fast intra-prediction scheme for VVC screen content coding based on ultra-lightweight convolutional neural network (CNN). Firstly, an ultra-lightweight CNN is designed to segment the image into the natural content region (NCR) and the screen content region (SCR). Then, an adaptive intra-coding mode pruning scheme based on the ultra-lightweight CNN is proposed to accelerate the VVC screen content coding process. Finally, our proposed method was implemented into VTM-10.0 and experimental results show that our method can save averagely 7.68% and maximum to 13.62% of the encoding time without encoding performance loss.

关键词： Versatile Video Coding deep learning screen content encoding complexity

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Decision Tree Based Early Termination Algorithm for Affine Prediction in AVS3 25

Decision Tree Based Early Termination Algorithm for Affine P...

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25th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Wu, Jiacheng Sun, Songlin Zhang, Jiaqi Han, Xu Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9798350338935

The third generation of Audio Video Standard(AVS3) has adopted affine prediction(including affine merge mode and affine inter mode), which can characterize non-translational motions such as rotation, zooming, and shearing. Though affine prediction leads to BD-Rate saving, it also results in substantial computational complexity. In this paper, we propose a fast algorithm to address this problem. Some useful features are introduced and used to train five decision tree classifiers to skip unnecessary calculations in affine prediction. Experimental results show that compared to HPM 14.0, the proposed algorithm reduces 19% affine merge time, 18% affine inter time, and 6% encoding time, with negligible coding performance loss under random access(RA) configurations.

关键词： affine prediction machine learning encoding complexity inter prediction AVS3 video coding

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Fast QTMT decision tree for Versatile Video Coding based on deep neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第29期81卷 42731-42747页

作者： Abdallah, Bouthaina Belghith, Fatma Ben Ayed, Mohamed Ali Masmoudi, Nouri Univ Sfax Natl Engn Sch Sfax Elect & Informat Technol Lab Sfax 3035 Tunisia Univ Sfax Sfax Natl Sch Elect & Commun ENETCOM New Technol & Telecom Syst Lab NTSCOM Sfax Tunisia

Versatile Video Coding (VVC), the emerging video coding standard, outperforms the coding efficiency of the previous standard named High Efficiency Video Coding (HEVC) at the cost of an encoding complexity increase. In fact, VVC proposes a new partitioning block structure called quadtree with nested multi-type tree (QTMT) that introduces a more flexible partition shape compared to the previous splitting algorithms namely quadtree plus binary tree (QTBT) and quadtree (QT) structures adopted in HEVC. However, QTMT increases the encoding time due to the rate-distortion cost (RDcost) process. In order to overcome this issue, this paper proposes a fast intra partitioning algorithm based on a Deep Learning (DL) approach using a Convolution Neural Network (CNN). First, a fast QTMT partition algorithm based on a CNN-binary tree horizontal (CNN-BTH) network is developed to predict the BTH mode decision at 32x32 Coding Units (CUs). The BTV decision tree algorithm is also predicted at this level by a CNN-binary tree vertical (CNN-BTV). Then, two algorithms are combined to suggest a new fast intra QTMT decision tree algorithm. Compared to the VVC reference software VTM-3.0, the proposed overall intra QTMT partition approach reaches a significant complexity reduction down to 37% compared to the original software VTM-3.0, and an average of 31% in terms of encoding time saving with a slight loss in coding performance.

关键词： Versatile Video Coding (VVC) encoding complexity Quadtree with nested multi-type tree (QTMT) Convolution Neural Network (CNN)

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Comprehensive complexity Assessment of Emerging Learned Image Compression on CPU and GPU 48

Comprehensive Complexity Assessment of Emerging Learned Imag...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pakdaman, Farhad Gabbouj, Moncef Tampere University Faculty of Information Technology and Communication Sciences Finland University of Mazandaran Faculty of Engineering and Technology Babolsar*** Iran

ISBN: (纸本)9781728163277

Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compression efficiency comparable to state-of-the-art image compression, such as HEVC or even VVC. However, the existing solutions often require a huge computational complexity, which discourages their adoption in international standards or products. This paper provides a comprehensive complexity assessment of several notable methods, that shed light on the matter, and guide the future development of this field by presenting key findings. To do so, six existing methods have been evaluated for both encoding and decoding, on CPU and GPU platforms. Various aspects of complexity such as the overall complexity, share of each coding module, number of operations, number of parameters, most demanding GPU kernels, and memory requirements have been measured and compared on Kodak dataset. The reported results (1) quantify the complexity of LC methods, (2) fairly compare different methods, and (3) a major contribution of the work is identifying and quantifying the key factors affecting the complexity. © 2023 IEEE.

关键词： complexity assessment encoding complexity image coding Learned compression

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Fast Multi-Type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 4388-4399页

作者： Park, Sang-hyo Kang, Je-Won Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Ewha Womans Univ Dept Elect & Elect Engn Seoul South Korea Ewha Womans Univ Smart Factory Multidisciplinary Program Seoul South Korea

In this paper, we propose a fast decision scheme using a lightweight neural network (LNN) to avoid redundant block partitioning in versatile video coding (VVC). A more versatile block structure, named the multi-type tree (MTT) structure, which includes binary trees (BTs) and ternary trees (TTs), is adopted by VCC, in addition to the traditional quadtree structure. The MTT improved the coding efficiency compared with previous video coding standards. However, the new tree structures, mainly TT, significantly increased the complexity of the VVC encoder. Although widespread application of VVC has been inhibited, this problem has not yet been investigated thoroughly in the literature. In this study, we first determine the statistical characteristics of coded parameters that exhibit correlation with the TT and develop two useful types of features-explicit VVC features (EVFs) and derived VVC features (DVFs)-to facilitate the intra coding of VVC. These features can be obtained efficiently during the intra prediction before the determination of the best block partitioning during rate-distortion optimization in VVC encoding. Our LNN model decides whether to terminate the nested TT block structures subsequent to a quadtree based on the features. The experimental results confirm that the proposed method substantially decreases the encoding complexity of VVC with a slight coding loss under the All Intra configuration. Our code, models, and dataset are available at https://***/foriamweak/MTTPartitioning_LNN.

关键词： encoding complexity theory Image coding Transforms Tools Standards Quantization (signal) Block partitioning Deep learning encoding complexity Image compression Intra prediction Multi-type tree Neural network Video compression VVC

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Study on Machine Learning Models for Tree Partitioning Method of Versatile Video Coding

Study on Machine Learning Models for Tree Partitioning Metho...

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International Conference on Electronics, Information, and Communication (ICEIC)

作者： Lee, Sujin Park, Sang-Hyo Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9781665409346

In this paper, we propose a method using machine learning models to determine necessity of ternary tree (TT) partitioning in versatile video coding (VVC). To reduce the encoding complexity of a multi-type tree (MTT) partitioning in VVC, it is significantly important to early decide whether TT is needed. In this study, we analyze the correlation between the known features and the TT partitioning using extensive video dataset. We present a comparative study on machine learning models that consider the TT decision process as a binary classification problem. The experimental results show that the proposed model achieves higher accuracy than that of the existing model. Our code and dataset are available at https://***/sujineel/ICEIC_2022.

关键词： Machine learning VVC video compression decision tree encoding complexity

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A Multirate Fully Parallel LDPC Encoder for the IEEE 802.11n/ac/ax QC-LDPC Codes Based on Reduced complexity XOR Trees

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2021年第1期29卷 51-64页

作者： Mahdi, Ahmed Kanistras, Nikos Paliouras, Vassilis Univ Thessaly Elect & Comp Engn Dept Volos 38221 Greece Univ Patras Elect & Comp Engn Dept VLSI Design Lab Patras 26500 Greece

This article proposes an encoding method based on a two-step encoding algorithm for the 12 quasi-cyclic (QC)-low-density parity-check (LDPC) (QC-LDPC) codes specified in the IEEE 802.11n/ac/ax standards. The proposed approach jointly considers all codes of the particular set, instead of targeting each code separately. The proposed algorithm performs multiplication by inverse matrices. The complexity of the multiplications is significantly reduced by the introduced encoding method. It allows the implementation of full-parallel architectures that execute the encoding process within a single clock cycle, or more for pipelined implementations, for any of the supported codes. A corresponding VLSI encoding architecture based on XOR-gate trees is also proposed. The proposed solution exploits the structure and features of the involved matrices to extract common subexpressions (CSs) using common sub-expression sharing techniques (CSST). Such expressions result due to common features of the original matrices and the corresponding inverses, identified in this article. Innovative subexpression extraction procedures that target the specific codes as a set are introduced here. Furthermore, illustrative single-clock hardware encoders derived by the proposed technique are integrated into 90- and 45-nm technologies at 1 GHz occupying 125 and 107 KGates, respectively, achieving throughput rates up to 1.62 Tbps.

关键词： encoding Parity check codes Computer architecture Phase change materials Hardware complexity theory Wireless fidelity Common subexpression (CS) complexity reduction encoding complexity low-density parity-check (LDPC) encoding matrix inversion multi-Gbps throughput rate quasi-cyclic (QC) LDPC VLSI architecture

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Object-Cooperated Ternary Tree Partitioning Decision Method for Versatile Video Coding

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SENSORS 2022年第17期22卷 6328-6328页

作者： Lee, Sujin Park, Sang-hyo Jun, Dongsan Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Dong A Univ Dept Comp Engn Busan 49315 South Korea

In this paper, we propose an object-cooperated decision method for efficient ternary tree (TT) partitioning that reduces the encoding complexity of versatile video coding (VVC). In most previous studies, the VVC complexity was reduced using decision schemes based on the encoding context, which do not apply object detecion models. We assume that high-level objects are important for deciding whether complex TT partitioning is required because they can provide hints on the characteristics of a video. Herein, we apply an object detection model that discovers and extracts the high-level object features-the number and ratio of objects from frames in a video sequence. Using the extracted features, we propose machine learning (ML)-based classifiers for each TT-split direction to efficiently reduce the encoding complexity of VVC and decide whether the TT-split process can be skipped in the vertical or horizontal direction. The TT-split decision of classifiers is formulated as a binary classification problem. Experimental results show that the proposed method more effectively decreases the encoding complexity of VVC than a state-of-the-art model based on ML.

关键词： ternary tree versatile video coding object detection encoding complexity machine learning

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