Paper Info
Title
Framework And Vlsi Architecture Of Measurement-Domain Intra Prediction For Compressively Sensed Visual Contents
Abstract
This paper presents a measurement-domain intra prediction coding framework that is compatible with compressive sensing (CS)-based image sensors. In this framework, we propose a low-complexity intra prediction algorithm that can be directly applied to measurements captured by the image sensor. We proposed a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Furthermore, a low-cost Very Large Scale Integration (VLSI) architecture is implemented for the proposed framework, by substituting the matrix multiplication with shared adders and shifters. The experimental results show that our proposed framework can compress the measurements and increase coding efficiency, with 34.9% BD-rate reduction compared to the direct output of CS-based sensors. The VLSI architecture of the proposed framework is 9.1 Kin area, and achieves the 83% reduction in size of memory bandwidth and storage for the line buffer. This could significantly reduce both the energy consumption and bandwidth in communication of wireless camera systems, which are expected to be massively deployed in the Internet of Things (IoT) era.
Year
DOI
Venue
2017
10.1587/transfun.E100.A.2869
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Keywords
Field
DocType
compressed sensing, intra prediction, structured measurement matrix, measurement-domain prediction
Theoretical computer science,Computer engineering,Compressed sensing,Mathematics,Vlsi architecture
Journal
Volume
Issue
ISSN
E100A
12
0916-8508
Citations 
PageRank 
References 
0
0.34
7
Authors
5
Name
Order
Citations
PageRank
Jian-Bin Zhou1234.66
Dajiang Zhou235550.25
Li Guo3154.12
Takeshi Yoshimura44313.56
Satoshi Goto5144.21