Enhancement Layer Coding for Chroma Sub-Sampled Screen Content Video

Heindel, Andreas; Prestele, Benjamin; Gehlert, Alexander; Kaup, André

doi:10.1109/tcsvt.2021.3061944

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…In this sampling methodology, all three YCbCr components have the same sampling rate as the input resolution. [65,66] 4 : 2 : 2 In this sampling methodology, chroma subcomponents are sampled by a factor of 2, and their influential position is co-sited. [65,66] 4 : 1 : 1 In this sampling methodology, Cb and Cr components are sampled by a factor of 4 horizontally and co-sited with the fourth brightness sample.…”

Section: : :mentioning

confidence: 99%

“…[65,66] 4 : 2 : 2 In this sampling methodology, chroma subcomponents are sampled by a factor of 2, and their influential position is co-sited. [65,66] 4 : 1 : 1 In this sampling methodology, Cb and Cr components are sampled by a factor of 4 horizontally and co-sited with the fourth brightness sample. [65,66] 4 : 2 : 0 In this sampling methodology, Cb and Cr components are subsampled by a factor of 2 in both the horizontal and vertical directions.…”

Section: : :mentioning

confidence: 99%

“…[65,66] 4 : 1 : 1 In this sampling methodology, Cb and Cr components are sampled by a factor of 4 horizontally and co-sited with the fourth brightness sample. [65,66] 4 : 2 : 0 In this sampling methodology, Cb and Cr components are subsampled by a factor of 2 in both the horizontal and vertical directions. [65,66] 4 : 1 : 0 This sampling methodology uses half of the vertical and one-fourth of the horizontal color resolution along with one-eighthof the bandwidth of the maximum color resolution.…”

Section: : :mentioning

confidence: 99%

“…[65,66] 4 : 2 : 0 In this sampling methodology, Cb and Cr components are subsampled by a factor of 2 in both the horizontal and vertical directions. [65,66] 4 : 1 : 0 This sampling methodology uses half of the vertical and one-fourth of the horizontal color resolution along with one-eighthof the bandwidth of the maximum color resolution. [65,66] 3 : 1 : 1…”

Section: : :mentioning

confidence: 99%

“…[65,66] 4 : 1 : 0 This sampling methodology uses half of the vertical and one-fourth of the horizontal color resolution along with one-eighthof the bandwidth of the maximum color resolution. [65,66] 3 : 1 : 1…”

Section: : :mentioning

confidence: 99%

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Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

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Vidyarthi

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In response to the COVID-19 pandemic and its strain on healthcare resources, this study presents a comprehensive review of various techniques that can be used to integrate image compression techniques and statistical texture analysis to optimize the storage of Digital Imaging and Communications in Medicine (DICOM) files. In evaluating four predominant image compression algorithms, i.e., discrete cosine transform (DCT), discrete wavelet transform (DWT), the fractal compression algorithm (FCA), and the vector quantization algorithm (VQA), this study focuses on their ability to compress data while preserving essential texture features such as contrast, correlation, angular second moment (ASM), and inverse difference moment (IDM). A pivotal observation concerns the direction-independent Grey Level Co-occurrence Matrix (GLCM) in DICOM analysis, which reveals intriguing variations between two intermediate scans measured with texture characteristics. Performance-wise, the DCT, DWT, FCA, and VQA algorithms achieved minimum compression ratios (CRs) of 27.87, 37.91, 33.26, and 27.39, respectively, with maximum CRs at 34.48, 68.96, 60.60, and 38.74. This study also undertook a statistical analysis of distinct CT chest scans from COVID-19 patients, highlighting evolving texture patterns. Finally, this work underscores the potential of coupling image compression and texture feature quantification for monitoring changes related to human chest conditions, offering a promising avenue for efficient storage and diagnostic assessment of critical medical imaging.

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confidence: 99%

Section: : :mentioning

confidence: 99%

Section: : :mentioning

confidence: 99%

Section: : :mentioning

confidence: 99%

Section: : :mentioning

confidence: 99%

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Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Shakya,

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Joint Content Caching, Recommendation, and Transmission for Layered Scalable Videos Over Dynamic Cellular Networks: A Dueling Deep Q-Learning Approach

Xie,

Jia,

et al. 2024

IEEE Access

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Scalable Video Coding (SVC) and edge caching are two techniques that hold the potential to improve user-perceived video viewing experience. Moreover, video recommendation can further enhance the caching gain by reshaping users' video preferences. In this paper, we investigate the video caching, recommendation and transmission for layered SVC streaming in cache-enabled cellular networks. Considering the dynamic characteristics of video popularity distribution and wireless network environment, to improve energy efficiency by minimizing system energy consumption and ensure the average user preference deviation tolerance, we begin by formulating a long-term optimization problem that focuses on video caching, recommendation and user association (UA). The problem is then transformed into a Markov decision process (MDP), which is solved by designing a dueling deep Q-learning network (DDQN)based algorithm. Using this algorithm, we can obtain the optimal video caching, recommendation and UA solutions. Since the action space of the MDP is huge, to cope with the "curse of dimensionality", linear approximation is integrated into the designed algorithm. Finally, the proposed algorithm's convergence and effectiveness in reducing long-term system energy consumption are demonstrated through extensive simulations.INDEX TERMS Scalable video coding; edge caching; recommendation; user association; energy efficiency; dueling deep Q-learning.

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Structural Quality Assured Global Optimization for CTU-Level Rate Control of Screen Content Coding

Tang,

Tan,

Ding

et al. 2023

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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Enhancement Layer Coding for Chroma Sub-Sampled Screen Content Video

Cited by 3 publications

References 13 publications

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Joint Content Caching, Recommendation, and Transmission for Layered Scalable Videos Over Dynamic Cellular Networks: A Dueling Deep Q-Learning Approach

Structural Quality Assured Global Optimization for CTU-Level Rate Control of Screen Content Coding

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