Video Analytical Coding: When Video Coding Meets Video Analysis

Autor: Frederic Dufaux, Ce Zhu, Mao Min, Fangliang Song, Yuyang Liu, Xiang Zhang
Přispěvatelé: University of Electronic Science and Technology of China (UESTC), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), National Natural Science Foundation of China (61571102), Other Funds (2015AA015903, ZYGX2014Z003)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Computer science
media_common.quotation_subject
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Fidelity
02 engineering and technology
Video quality
Video analysis
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
0202 electrical engineering
electronic engineering
information engineering
Computer vision
Electrical and Electronic Engineering
media_common
business.industry
video analytical coding
Quantization (signal processing)
Available bit rate
020206 networking & telecommunications
analytical distortion
Object detection
Rate–distortion optimization
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Signal Processing
Human visual system model
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Coding (social sciences)
rate distortion optimization
Zdroj: Signal Processing: Image Communication
Signal Processing: Image Communication, Elsevier, 2018, 67, pp.48-57. ⟨10.1016/j.image.2018.05.012⟩
ISSN: 0923-5965
1879-2677
DOI: 10.1016/j.image.2018.05.012⟩
Popis: International audience; Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate–distortion optimization techniques by maximizing a fidelity cost function (e.g., peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term “analytical distortion” to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate–analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1-measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion).
Databáze: OpenAIRE
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