Quality-aware images.
We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.
Wang
Zhou
Z
Center for Neural Science, New York University, NY 10012, USA. zhouwang@ieee.org
Wu
Guixing
G
Sheikh
Hamid Rahim
HR
Simoncelli
Eero P
EP
Yang
En-Hui
EH
Bovik
Alan Conrad
AC
D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D011786:Quality Control; D012815:Signal Processing, Computer-Assisted