IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1680-1689, June 2006

Quality-Aware Images

Zhou Wang¹, Guixing Wu², Hamid R. Sheikh³, Eero P. Simoncelli¹ En-Hui Yang², and Alan C. Bovik⁴

¹Laboratory for Computational Vision (LCV), New York University, New York, NY

²Dept. of Electrical and Computer Engineering, University of Waterloo, Canada

³Texas Instrument, Inc. Dallas, TX

⁴Laboratory for Image and Video Engineering (LIVE), The University of Texas at Austin, Austin, TX

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Abstract: 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. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/qaware/.

Index Terms – quality-aware image, image quality assessment, reduced-reference image quality assessment, natural image statistics, generalized Gaussian density, information hiding, image watermarking, image communication