Open AccessPerceptual hash algorithm‐based adaptive GOP selection algorithm for distributed compressive video sensing
Author(s) -
Chen Can,
Ding Fei,
Zhang Dengyin
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0354
Subject(s) - computer science , hash function , algorithm , selection (genetic algorithm) , compressed sensing , selection algorithm , artificial intelligence , pattern recognition (psychology) , computer vision , computer security
Distributed compressive video sensing (DCVS) is a novel video coding technique that shifts sophisticated motion estimation and compensation from the encoder to the decoder and is suitable for resource‐limited communication, namely wireless video sensor networks (WVSNs). In DCVS, key frames serve as the reference for subsequent non‐key frames in a given group of pictures (GOP). However, in fast‐motion sequences, e.g. scene‐changing sequences, a fixed GOP size can cause inaccuracy in the selection of reference key frames. The difference in the peak signal‐to‐noise ratio between key frames and non‐key frames caused by this inaccuracy appears as flicker in the decoded video, negatively affecting the quality of experience. To address this problem, the authors present a perceptual hash algorithm‐based adaptive GOP selection algorithm for DCVS and a novel allocation model for the frame sampling rate. In addition, the authors define several indexes to assess the degree of flicker in decoded video. The experimental results demonstrate that the proposed algorithm reduces the degree of flicker in fast‐motion sequences by 40–60% relative to the state‐of‐the‐art architecture, while also outperforming other adaptive GOP selection strategies.