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上传时间: 2016-01-07
详细说明： we develop an efficient BLIINDS-II algorithm using NSS approach in DCT domain for image and video quality assessment with no reference image. The approach relies on a simple Bayesian inference model to predict image and video quality score, after a set of features is extracted from an image. These features are extracted from a generalized NSS based model of local DCT coefficients. Generalized Gaussian density model parameters are used to form these features. BLIINDS-II (Blind Image Integrity notator using DCT Statistics-II) adop ts a simple probabilistic model for score prediction. Given the extracted features from a test image/video, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image/video. When tested on the LIVE IQA database, BLIINDS-II correlates highly with the human judgments of quality. ...展开收缩
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