NDLI: Perceptual hash algorithm-based adaptive GOP selection algorithm for distributed compressive video sensing

Content Provider	IET Digital Library
Author	Chen, Can Ding, Fei Zhang, Dengyin
Abstract	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.
Starting Page	210
Ending Page	217
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 2, Feb (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0354
Journal	IET Image Processing
Publisher Date	2017-09-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive GOP Selection Algorithm Compressed Sensing DCVS Decoder Distributed Compressive Video Sensing Encoder Fast-motion Sequence Fixed GOP Size Frame Sampling Rate Group of Pictures Image And Video Coding Image Sequence Motion Compensation Motion Estimation Novel Allocation Model Peak Signal-to-noise Ratio Perceptual Hash Algorithm Quality of Experience Reference Key Frame Selection Resource Allocation Resource-limited Communication Scene-changing Sequence Subsequent Nonkey Frames Video Coding Video Coding Technique Video Signal Processing Wireless Video Sensor Network WVSN
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Distributed compressed video sensing (2009)

An Improved Distributed Compressive Video Sensing Based on Adaptive Sparse Basis

Two-layer motion estimation algorithm for video coding

A parity-based error control method for distributed compressive video sensing

Adaptive key frame rate allocation for distributed video coding.

Adaptive low-complexity motion estimation algorithm for high efficiency video coding encoder

Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing

ADAPTIVE KEY FRAME RATE ALLOCATION FOR DISTRIBUTED VIDEO CODING

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Perceptual hash algorithm-based adaptive GOP selection algorithm for distributed compressive video sensing

Similar Documents

Distributed compressed video sensing (2009)

An Improved Distributed Compressive Video Sensing Based on Adaptive Sparse Basis

Two-layer motion estimation algorithm for video coding

A parity-based error control method for distributed compressive video sensing

Adaptive key frame rate allocation for distributed video coding.

Adaptive low-complexity motion estimation algorithm for high efficiency video coding encoder

Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing

ADAPTIVE KEY FRAME RATE ALLOCATION FOR DISTRIBUTED VIDEO CODING

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Perceptual hash algorithm-based adaptive GOP selection algorithm for distributed compressive video sensing