No-reference quality assessment for DCT-based compressed image

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WANG, Ci, Minmin SHEN, Chen YAO, 2015. No-reference quality assessment for DCT-based compressed image. In: Journal of Visual Communication and Image Representation. 28, pp. 53-59. ISSN 1047-3203. eISSN 1095-9076. Available under: doi: 10.1016/j.jvcir.2015.01.006

@article{Wang2015Noref-30316, title={No-reference quality assessment for DCT-based compressed image}, year={2015}, doi={10.1016/j.jvcir.2015.01.006}, volume={28}, issn={1047-3203}, journal={Journal of Visual Communication and Image Representation}, pages={53--59}, author={Wang, Ci and Shen, Minmin and Yao, Chen} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Shen, Minmin</dc:creator> <dcterms:isPartOf rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Wang, Ci</dc:creator> <dc:date rdf:datatype="">2015-03-16T15:33:28Z</dc:date> <dc:language>eng</dc:language> <dcterms:issued>2015</dcterms:issued> <bibo:uri rdf:resource=""/> <dcterms:isPartOf rdf:resource=""/> <dcterms:abstract xml:lang="eng">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.</dcterms:abstract> <dc:contributor>Wang, Ci</dc:contributor> <dc:creator>Yao, Chen</dc:creator> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:title>No-reference quality assessment for DCT-based compressed image</dcterms:title> <dc:contributor>Shen, Minmin</dc:contributor> <dc:contributor>Yao, Chen</dc:contributor> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:available rdf:datatype="">2015-03-16T15:33:28Z</dcterms:available> </rdf:Description> </rdf:RDF>

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