In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

Abstract-In this paper, we propose a new limit that promises theoretically achievable data reduction ratios up to approximately 9:1 with no perceptual loss in typical scenarios. Also, we introduce a novel Gaussian foveation scheme that provides experimentally achievable gains up to approximately 2 times the compression ratio of typical compression schemes with less perceptual loss than in typical transmissions. Both the proposed limit and foveation scheme shares the same background material: a model of image projection onto the retina; a model of cones distribution; and, subsequently, a proposed pointwise retina-based constraint called pixel efficiency. Quantitatively, the lattermost is globally processed to reveal the perceptual efficiency of a display. Analytical results indicate that in general the perceptual efficiency of displays are low for typical image sizes and viewing distances. Qualitatively, the pixel efficiency is used together with a lossy parameter to locally control the spatial resolution of a foveated image. Practical results show that proper use of the lossy parameter in the foveation filtering can increase the subjective quality of images.