Paper Info
Title
Perceptually optimized sparse coding for HDR images via divisive normalization
Abstract
High dynamic range (HDR) imaging techniques have been widely advocated that could shape next generation of digital photography. However, the popularity of HDR contents is hindered by the lack of displaying devices for rendering HDR images which could be very expensive. To tackle this, extensive tone-mapping operators (TMOs) have been proposed in order for transforming HDR images to viewable low dynamic range (LDR), and also applied in the backward-compatibility based HDR compression. However, how to efficiently improve the compression performance based on the perceptual evaluation is seldom addressed. In this work, we first propose a quality evaluation index for measuring the quality of the LDR image with the access of pristine HDR image. Then a sparse coding framework for efficiently compressing the LDR image, which is generated from its HDR version using TMO, is presented. Finally the compression efficiency could be improved by jointly optimize the sparse coding process in terms of the proposed quality metric based on the divisive normalization mechanism. Extensive experiments have shown that the proposed scheme can improve the perceptual quality of the compressed LDR image.
Year
DOI
Venue
2016
10.1109/VCIP.2016.7805470
2016 Visual Communications and Image Processing (VCIP)
Keywords
Field
DocType
High dynamic range,sparse representation,perceptual optimization,divisive normalization
Computer vision,Digital photography,Normalization (statistics),Neural coding,Computer science,Sparse approximation,Low dynamic range,Artificial intelligence,Rendering (computer graphics),High dynamic range
Conference
ISBN
Citations 
PageRank 
978-1-5090-5317-9
1
0.40
References 
Authors
12
6
Name
Order
Citations
PageRank
Lijuan Xie194.01
Xiang Zhang28812.61
Shiqi Wang31281120.37
s l wang416142.09
Xinfeng Zhang519512.61
Siwei Ma62229203.42