A Pixel-Level Segmentation-Synthesis Framework for Dynamic Texture Video Compression

Wang, Suhong; Jia, Chuanmin; Zhang, Xinfeng; Wang, Shiqi; Ma, Siwei; Gao, Wen

doi:10.1109/tcsvt.2022.3170543

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…Graham proposed a two-dimensional contour coding in [56], which can be viewed as a predecessor of segmentation coding, and Biggar first formally utilized a segmented image coder with better performance than the transform coder in [57]. Since then, a variety of studies on segmentation-based coding have been performed, including segmentation-based coding [34,[58][59][60][61][62] and segmentation methods [63,64]. MBC methods mentioned above explore flexible and fine-grained partitions without considering the knowledge of objects or scenes in the world.…”

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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Section: Model-based Coding Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gao

Wang

et al. 2023

Vis. Intell.

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“…Ye Liu et al [13] presented a multi-scale structure-aware network for robust human parsing. The proposed network, named MSSA-Net, harnesses multi-scale features and joint structural cues in a coarse-to-fine strategy for effective human parsing.…”

Section: B Human Parser Modelsmentioning

confidence: 99%

Human Parsing Based on Deep Learning for Analyzing the Body Pressure Distribution to Be Used in Pressure Ulcer Risk Management and Prediction

2023

1st International Conference on Challenges in Engineering, Medical, Economics &Amp; Education: Research &Amp; Solutions (CEMEER

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Pressure ulcers, commonly referred to as pressure injuries, are prevalent skin injuries that occur in hospitalized patients. This paper investigates the utilization of deep learning-based models to detect human body parts from pressure heatmaps captured by sensor sheets. The primary contribution involves training and finetuning a human body parser capable of analyzing binary, gray-level, and colored heatmaps representing body pressure. The fine-tuned body parser exhibits satisfactory accuracy for the analysis of pressure ulcers. Furthermore, an action recognition architecture based on deep learning is applied to analyze the movement of each body part. This innovative approach establishes a reliable criterion for making informed decisions regarding the likelihood of pressure ulcers developing in organs subject to higher pressure and reduced movement.

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End-to-end video compression for surveillance and conference videos

Wang

Zhao²,

Gao³

et al. 2022

Multimed Tools Appl

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A Pixel-Level Segmentation-Synthesis Framework for Dynamic Texture Video Compression

Cited by 3 publications

References 51 publications

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

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

Human Parsing Based on Deep Learning for Analyzing the Body Pressure Distribution to Be Used in Pressure Ulcer Risk Management and Prediction

End-to-end video compression for surveillance and conference videos

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