Perceptually optimized sparse coding for HDR images via divisive normalization
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.