Milind S. Gide

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Textures are being extensively used in next-generation video coding, image restoration and computer graphics wherein the stochastic and perceptual properties of textures are exploited. Given the prominence of texture content in image and video processing applications, texture quality assessment and objective quality metrics geared specifically towards(More)
Existing saliency models have been designed and evaluated for predicting the saliency in distortion-free images. However, in practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression and transmission. Several studies have explored the effect of distortion on human visual(More)
One important application of computational saliency models is to aid objective image quality assessment. Given this, it is necessary to evaluate existing state-of-the-art visual attention models for their performance by comparing them with eye-tracking data associated with a quality assessment task. Existing comparative studies compare the saliency models(More)
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance(More)
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed over the past few years. These models are traditionally evaluated by using performance evaluation metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments(More)
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