A VVC Proposal With Quaternary Tree Plus Binary-Ternary Tree Coding Block Structure and Advanced Coding Techniques

Huang, Yu-Wen; Hsu, Chih‐Wei; Chen, Ching-Yeh; Chuang, T. J.; Hsiang, Shih-Ta; Chen, Chun-Chia; Chiang, Man-Shu; Lai, Chen-Yen; Tsai, Chia-Ming; Su, Yu-Chi; Lin, Zhiyi; Hsiao, Yu-Ling; Chubach, Olena; Lin, Yu‐Cheng; Lei, Shawmin

doi:10.1109/tcsvt.2019.2945048

Cited by 44 publications

(24 citation statements)

References 7 publications

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“…VVC inherits the Quadtree (QT) partitioning structure from HEVC and introduces the Multi-Type Tree (MTT) structure, resulting in the Quadtree with nested Multi-type Tree (QTMT). In VVC a CTU is first partitioned by a QT structure, then the quadtree structure can be further subdivided by an MTT structure [12]. As Fig.…”

Section: A Block Partitioningmentioning

confidence: 99%

“…As Fig. 3 illustrates, there are six partition types allowed in the QTMT structure, where a block, called coding unity (CU), can be defined as no split and the coding process is performed with the current CU size or the CU can be split using either the Quadtree, Binary tree, or Ternary tree structure [12]. The smallest allowed CU size is 4×4.…”

Section: A Block Partitioningmentioning

confidence: 99%

See 1 more Smart Citation

AV1 and VVC Video Codecs: Overview on Complexity Reduction and Hardware Design

Corrêa

Saldanha

Borges

et al. 2021

IEEE Open J. Circuits Syst.

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This article presents an extensive review of the state-of-the-art system-level solutions featuring complexity reduction and/or dedicated hardware designs for the AV1 and VVC video coding formats. These formats introduced several novel coding techniques compared to their predecessors to improve the coding efficiency at the cost of a significant computational cost. In this article, we discuss the main novelties of AV1 and VVC in each coding module, including block partitioning, intra and inter prediction, transform, entropy coding, and in-loop filters. Then, we present the main published works focusing on complexity reduction and hardware designs for AV1 and VVC. Most of the complexity reduction solutions target the complex and flexible block partitioning structures of these encoders to provide a better tradeoff between coding efficiency and complexity reduction whereas the hardware designs focus on the challenge of implementing the new coding tools to attend real-time processing of high-definition videos. Even with the presented works reaching impressive results, these research fields remain opened for innovative contributions, as discussed in this article.

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Section: A Block Partitioningmentioning

confidence: 99%

Section: A Block Partitioningmentioning

confidence: 99%

AV1 and VVC Video Codecs: Overview on Complexity Reduction and Hardware Design

Corrêa

Saldanha

Borges

et al. 2021

IEEE Open J. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…The most widely used inter-frame video compression formats such as HEVC (Sullivan et al, 2012), VVC (Huang et al, 2020), and VP9 (Mukherjee et al, 2013) all rely on a quadtree structure for their block matching process called adaptive block matching. The goal is to have variable block sizes that depend on the scene depicted in the frame.…”

Section: Compression Algorithmmentioning

confidence: 99%

Deep Learning-Based Object Tracking via Compressed Domain Residual Frames

Khoury

Samelson

Macq

2021

Front. Signal Process.

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The extensive rise of high-definition CCTV camera footage has stimulated both the data compression and the data analysis research fields. The increased awareness of citizens to the vulnerability of their private information, creates a third challenge for the video surveillance community that also has to encompass privacy protection. In this paper, we aim to tackle those needs by proposing a deep learning-based object tracking solution via compressed domain residual frames. The goal is to be able to provide a public and privacy-friendly image representation for data analysis. In this work, we explore a scenario where the tracking is achieved directly on a restricted part of the information extracted from the compressed domain. We utilize exclusively the residual frames already generated by the video compression codec to train and test our network. This very compact representation also acts as an information filter, which limits the amount of private information leakage in a video stream. We manage to show that using residual frames for deep learning-based object tracking can be just as effective as using classical decoded frames. More precisely, the use of residual frames is particularly beneficial in simple video surveillance scenarios with non-overlapping and continuous traffic.

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“…Its image division has evolved from a single, fixed division to a diverse and flexible division structure, which can more efficiently adapt to the encoding and decoding of high-resolution images. Among them, the QTMT division scheme [3] [4] is used in VVC to obtain better compression efficiency. The scheme has five division modes: Quad-Tree (QT), Binary-Tree-Vertical (BTV), Binary-Tree-Horizontal (BTH), Ternary-Tree-Vertical (TTV) and Ternary-Tree-Horizontal (TTH), which are more than HEVC in two ways, TTV and TTH, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast CU Decision-Making Algorithm Based on DenseNet Network for VVC

et al. 2021

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The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of 4×4 blocks in each 64×64 block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10% in VTM10.0 intra coding, while Bjøntegaard delta bit rate (BDBR) only increases by 1.86%. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81%, the BDBR loss only increased by 1.92%.

show abstract

A VVC Proposal With Quaternary Tree Plus Binary-Ternary Tree Coding Block Structure and Advanced Coding Techniques

Cited by 44 publications

References 7 publications

AV1 and VVC Video Codecs: Overview on Complexity Reduction and Hardware Design

AV1 and VVC Video Codecs: Overview on Complexity Reduction and Hardware Design

Deep Learning-Based Object Tracking via Compressed Domain Residual Frames

Fast CU Decision-Making Algorithm Based on DenseNet Network for VVC

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