Wavelet-based directional structural distortion model for image quality assessment
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It is a challenging work to find an efficient metric of image quality assessment, which is an important problem for many image processing tasks. In this paper, we propose a novel wavelet-based directional structural distortion model for image quality assessment, which explores the geometric structural features of natural image. The experimental results upon image database show that our proposed method is in accordance with characteristic of human visual system and has better consistency with the subjective assessment of human beings than current image quality assessment algorithms.
- H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms,” IEEE Trans. Image Process. 15(11), 3441–3345 (2006). CrossRef
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004). CrossRef
- Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-Scale Structural Similarity for Image Quality Assessment,” in Proceedings of IEEE Int. Conf. Signals, Systems, and Computers, Pacific Grove, 2003, pp. 1398–1402.
- D. M. Chandler and S. S. Hemami, “VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images,” IEEE Trans. Image Process. 16(9), 2284–2298 (2007). CrossRef
- H. R. Sheikh and A. C. Bovik, “Image Information and Visual Quality,” IEEE Trans, on Image Process. 15(2), 430–444 (2006). CrossRef
- A. M. Eskicioglu and P. S. Fisher, “Image Quality Measures and Their Performance,” IEEE Trans. Commun. 43(12), 2959–2965 (1995). CrossRef
- M. J. Nadenau, J. Reichel, and M. Kunt, “Wavelet-Based Color Image Compression: Exploiting the Contrast Sensitivity,” IEEE Trans. Image Process. 12(1), 58–70 (2003). CrossRef
- J. L. Mannos and D. J. Sakrison, “The Effect of Visual Fidelity Criterion on the Encoding of Images,” IEEE Trans. Inform. Theory 20(2), 525–536 (1974). CrossRef
- X. Ran and N. Farvardin, “Aperceptually Motivated Three-Component Image Model-Part I: Description of Model,” lEEE Trans. on Image Process. 4(4), 401–405 (1995). CrossRef
- E. L. Pennec and S. Mallat, “Sparse Geometric Image Representation with Bandelets,” IEEE Trans. Image Process. 14(4), 423–438 (2005). CrossRef
- G. H. Chen, C. L. Yang, and S. L. Xie, “Gradient-Based Structural Similarity for Image Quality Assessment,” in Proceedings of IEEE Int. Conf. Image Proc., Atlanta, USA, 2006, pp. 2929–2932.
- I. Daubechies, Ten Lectures on Wavelets (SIAM, 1992).
- H. R. Sheikh, Z. Wang, L. Cormackl, et al., “Live Image Quality Assessment Database Release,” http://live.ece.utexas.edu/research/quality.
- A. Webster and F. Speranza, “Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment,” http://www.vqeg.org.
- Wavelet-based directional structural distortion model for image quality assessment
Pattern Recognition and Image Analysis
Volume 20, Issue 3 , pp 286-292
- Cover Date
- Print ISSN
- Online ISSN
- SP MAIK Nauka/Interperiodica
- Additional Links
- Image quality assessment
- Human visual system
- Wavelet transform
- Directional structural similarity
- Industry Sectors