No-reference quality assessment for DCT-based compressed image |
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| Affiliation: | 1. East China Normal University, Department of Computer Science and Technology, China;2. University of Konstanz, INCIDE Center, Germany;3. South China University of Technology, School of Software Engineering, China;4. The Third Research Institute of Ministry of Public Security, China;5. Shanghai Key Laboratory of Digital Media Processing and Transmission, China;1. School of Mathematics and Qilu Securities Institute for Financial Studies, Shandong University, China;2. Department of Mathematics and Statistics, University of Konstanz, Germany;3. School of Mathematics, Shandong University, China;1. Fachbereich Philosophie, Universität Konstanz, 78457 Konstanz, Germany;2. Institute for Theoretical Physics, Albert Einstein Center for Fundamental Physics, Universität Bern, Sidlerstrasse 5, 3012 Bern, Switzerland;1. Department of Physics, University of Konstanz, D-78457 Konstanz, Germany;2. Center for Free-Electron Laser Science/DESY, D-22607 Hamburg, Germany;3. Department of Chemistry, Sungkyunkwan University, 440-746 Suwon, Republic of Korea;1. Department of Decision Sciences and IGIER, U. Bocconi, Italy;2. Department of Mathematics and Statistics, U. Konstanz, Germany;3. Morgan Stanley, United Kingdom |
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| Abstract: | A blind/no-reference (NR) method is proposed in this paper for image quality assessment (IQA) of the images compressed in discrete cosine transform (DCT) domain. When an image is measured by structural similarity (SSIM), two variances, i.e. mean intensity and variance of the image, are used as features. However, the parameters of original copies are actually unavailable in NR applications; hence SSIM is not widely applicable. To extend SSIM in general cases, we apply Gaussian model to fit quantization noise in spatial domain, and directly estimate noise distribution from the compressed version. Benefit from this rearrangement, the revised SSIM does not require original image as the reference. Heavy compression always results in some zero-value DCT coefficients, which need to be compensated for more accurate parameter estimate. By studying the quantization process, a machine-learning based algorithm is proposed to estimate quantization noise taking image content into consideration. Compared with state-of-the-art algorithms, the proposed IQA is more heuristic and efficient. With some experimental results, we verify that the proposed algorithm (provided no reference image) achieves comparable efficacy to some full reference (FR) methods (provided the reference image), such as SSIM. |
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| Keywords: | Compression distortion Probability model No-reference estimate Objective quality assessment Image quality assessment Gaussian distribution Uniform distribution Noise variance |
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