IEEE Trans Image Process - No-reference image quality assessment using visual codebooks.

Tópicos

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Resumo

The goal of no-reference objective image quality assessment (NR-IQA) is to develop a computational model that can predict the human-perceived quality of distorted images accurately and automatically without any prior knowledge of reference images. Most existing NR-IQA approaches are distortion specific and are typically limited to one or two specific types of distortions. In most practical applications, however, information about the distortion type is not really available. In this paper, we propose a general-purpose NR-IQA approach based on visual codebooks. A visual codebook consisting of Gabor-filter-based local features extracted from local image patches is used to capture complex statistics of a natural image. The codebook encodes statistics by quantizing the feature space and accumulating histograms of patch appearances. This method does not assume any specific types of distortions; however, when evaluating images with a particular type of distortion, it does require examples with the same or similar distortion for training. Experimental results demonstrate that the predicted quality score using our method is consistent with human-perceived image quality. The proposed method is comparable to state-of-the-art general-purpose NR-IQA methods and outperforms the full-reference image quality metrics, peak signal-to-noise ratio and structural similarity index on the Laboratory for Image and Video Engineering IQA database.

Resumo Limpo

goal norefer object imag qualiti assess nriqa develop comput model can predict humanperceiv qualiti distort imag accur automat without prior knowledg refer imag exist nriqa approach distort specif typic limit one two specif type distort practic applic howev inform distort type realli avail paper propos generalpurpos nriqa approach base visual codebook visual codebook consist gaborfilterbas local featur extract local imag patch use captur complex statist natur imag codebook encod statist quantiz featur space accumul histogram patch appear method assum specif type distort howev evalu imag particular type distort requir exampl similar distort train experiment result demonstr predict qualiti score use method consist humanperceiv imag qualiti propos method compar stateoftheart generalpurpos nriqa method outperform fullrefer imag qualiti metric peak signaltonois ratio structur similar index laboratori imag video engin iqa databas

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