Ruigang Fang

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With the effort done by many researchers on 2D image quality assessment, the correlation between subjectivity scores in the databases and the objective scores obtained by current image quality metrics reaches to a high level. However, stereoscopic image quality metrics do not achieve a good performance on 3D image databases. It's important to take advantage(More)
In this paper, we present a no-reference image quality assessment method, which we call BNB (an acronym for Blurriness, Noisiness, Blockiness). Our BNB method quantifies blurriness, noisiness and blockiness of a given image, which are considered three critical factors that affect users' quality of experience (QoE). The well designed BNB metrics are based on(More)
Image viewing distance plays an important role in the assessment of image Quality of Experience (QoE). In this work, we present a subjective image QoE study in which a total of 494 images evaluated by more than 30 human subjects at 7 different viewing distance. Through the study, we observed that different images have different regularities between viewing(More)
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