Humans are the end users to visual media. Therefore, in order to develop an effective
quantitative method for visual computing, we must take into account how humans perceive visual
quality. Recently, on the basis of human visual saliency information, H. Alers et al. showed that
the quality of the foreground (FG) of an image is more important than that of its background (BG)
in visual quality assessment. Therefore, in image compression an adaptive bitrate allocation that
favors the image foreground can be expected to increase the visual quality of decoded images.
Two approaches for JPEG image compression will be discussed. Firstly, we consider a bi-level
saliency based coding based on foreground/background image segmentation. However, this approach has
discontinuous visual quality at borders between FG and BG blocks. Secondly, in our multi-level
saliency based approach the bitrate is adapted blockwise and offers smooth visual quality. In order
to evaluate our approach by subjective image quality assessment, we performed a very large
crowdsourcing study using pairwise comparisons for a set of 44 image sources at five bitrates in
each case comparing 12 different coding strategies with the standard JPEG coded image. Our results
show a significant improvement of subjective visual quality.
Sung-Hwan Jung received a Ph. D. in Electronic Engineering from Kyungpook National University,
Korea in 1988. From 1992 to 1994, he was a postdoctoral research staff of the Department of
Electrical and Computer Engineering at the University of California at Santa Barbara (UCSB). From
1999 to 2000, he also worked for the Colorado School of Mine (CSM) in Golden, Colorado. From 2008
to 2009, he had experience on the medical information processing at the Dental School of the
University of Missouri at Kansas City (UMKC). His research interests include content-based image
retrieval, steganography, watermarking, internet-based remote monitoring, medical image processing,
computer vision and pattern recognition, etc.