The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Image quality assessment traditionally means the comparison of original image with its distorted version using conventional methods like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR). In case of Blind Quality Evaluation with no prior knowledge about the image, a single parameter becomes insufficient to define the overall image quality. This paper proposes a quality metric based on sharpness of the image, presence of noise, overall contrast and luminance of the image and the detection of the eyes. Experimental results reveal that the proposed metric has strong relevance with human quality perception.