Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding

Meng, Xuewei; Jia, Chuanmin; Zhang, Xinfeng; Wang, Shiqi; Ma, Siwei

doi:10.1109/tip.2021.3126420

Cited by 7 publications

(2 citation statements)

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“…However, the rectangular partition of blockbased coding is rigid and inefficient for modeling irregular visual signals. As a variation of the block-based motion model, more flexible geometric partitions were proposed for motion compensation, including deformable blocks [49], meshes [50,51] and triangles [52], and they were also studied for H.264/AVC [53,54], HEVC [55] and VVC [33]. Although geometric partitions are flexible, they are also constrained by their fixed patterns.…”

Section: Model-based Coding Methodsmentioning

confidence: 99%

“…For example, in [32], a background picture model-based surveillance video coding method shows at least twice the compression ratio on surveillance videos of the AVC high profile. Moreover, other model-based coding methods display great potential nowadays and achieve obvious improvement over the traditional hybrid coding methods, such as geometric partition video coding [33] and segmentationbased coding [34]. Some MBC methods were also introduced into various coding standards, such as MPEG-4/7, AVS2, HEVC, and VVC.…”

Section: Introductionmentioning

confidence: 99%

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Overview of intelligent video coding: from model-based to learning-based approaches

Gao

Wang

et al. 2023

Vis. Intell.

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Intelligent video coding (IVC), which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics, includes visual content compact representation models and methods enabling structural, detailed descriptions of visual information at different granularity levels (i.e., block, mesh, region, and object) and in different areas. It aims to support and facilitate a wide range of applications, such as visual media coding, content broadcasting, and ubiquitous multimedia computing. We present a high-level overview of the IVC technology from model-based coding (MBC) to learning-based coding (LBC). MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components. Thanks to emerging deep learning technologies such as neural networks and generative models, LBC has become a rising topic in the coding area. In this paper, we first review the classical MBC approaches, followed by the LBC approaches for image and video data. We also discuss and overview our recent attempts at neural coding approaches, which are inspiring for both academic research and industrial implementation. Some critical yet less studied issues are discussed at the end of this paper.

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