Blind image quality assessment

Blind image quality assessment refers to the problem of evaluating the visual quality of an image without any reference. It addresses a fundamental distinction between fidelity and quality, i.e. human vision system usually does not need any reference to determine the subjective quality of a target image. In this paper, we propose to appraise the image quality by three objective measures: edge sharpness level, random noise level and structural noise level. They jointly provide a heuristic approach of characterizing the most important aspects of visual quality. We investigate various mathematical tools (analytical, statistical and PDE-based) for accurately and robustly estimating those three levels. Extensive experiment results are used to justify the validity of our approach.

[1]  Andrew B. Watson,et al.  DCT quantization matrices visually optimized for individual images , 1993, Electronic Imaging.

[2]  Patrick C. Teo,et al.  A model of perceptual image fidelity , 1995, Proceedings., International Conference on Image Processing.

[3]  P.J.L. van Beek,et al.  Edge-Based Image Representation and Coding , 1995 .

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Truong Q. Nguyen,et al.  Maximum-likelihood parameter estimation for image ringing-artifact removal , 2001, IEEE Trans. Circuits Syst. Video Technol..

[6]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[7]  Jyh-Charn Liu,et al.  Decision-based median filter improved by predictions , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[8]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[9]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[10]  Wilson S. Geisler,et al.  Image quality assessment based on a degradation model , 2000, IEEE Trans. Image Process..

[11]  Xin Li,et al.  Low bit rate image coding in the scale space , 2002, Proceedings DCC 2002. Data Compression Conference.

[12]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[13]  Kannan Ramchandran,et al.  A simple algorithm for removing blocking artifacts in block-transform coded images , 1998, IEEE Signal Processing Letters.

[14]  David S. Taubman,et al.  High performance scalable image compression with EBCOT , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[15]  Michael T. Orchard,et al.  Edge directed prediction for lossless compression of natural images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[16]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[17]  Gary E. Ford,et al.  The evolution of mean curvature in image filtering , 1994, Proceedings of 1st International Conference on Image Processing.