Studies of visual masking have provided a wide range of important insights into the processes involved in visual coding. However, very few of these studies have employed natural scenes as masks. Little is known on how the particular features found in natural scenes affect visual detection thresholds and how the results obtained using unnatural masks relate to the results obtained using natural masks. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds for a database of natural images. Via a three-alternative forced-choice experiment, we measured thresholds for detecting 3.7 cycles/8 vertically oriented log-Gabor noise targets placed within an 85 · 85pixels patch (1.98 patch) drawn from 30 natural images from the CSIQ image database (Larson & Chandler, Journal of Electronic Imaging, 2010). Thus, for each image, we obtained a masking map in which each entry in the map denotes the root mean squared contrast threshold for detecting the log-Gabor noise target at the corresponding spatial location in the image. From qualitative observations we found that detection thresholds were affected by several patch properties such as visual complexity, fineness of textures, sharpness, and overall luminance. Our quantitative analysis shows that except for the sharpness measure (correlation coefficient of 0.7), the other tested low-level mask features showed a weak correlation (correlation coefficients less than or equal to 0.52) with the detection thresholds. Furthermore, we evaluated the performance of a computational contrast gain control model that performed fairly well with an average correlation coefficient of 0.79 in predicting the local contrast detection thresholds. We also describe specific choices of parameters for the gain control model. The objective of this database is to provide researchers with a large ground-truth dataset in order to further investigate the properties of the human visual system using natural masks.

Fast prediction models of local distortion visibility and local quality can potentially make modern spatiotemporally adaptive coding schemes feasible for real-time applications. In this paper, a fast convolutional-neuralnetwork based quantization strategy for HEVC is proposed. Local artifact visibility is predicted via a network trained on data derived from our improved contrast gain control model. The contrast gain control model was trained on our recent database of local distortion visibility in natural scenes [Alam et al. JOV 2014]. Furthermore, a structural facilitation model was proposed to capture effects of recognizable structures on distortion visibility via the contrast gain control model. Our results provide on average 11% improvements in compression efficiency for spatial luma channel of HEVC while requiring almost one hundredth of the computational time of an equivalent gain control model. Our work opens the doors for similar techniques which may work for different forthcoming compression standards.

Image quality assessment has been a topic of recent intense research due to its usefulness in a wide variety of applications. Owing in large part to efforts within the HVEI community, image-quality research has particularly benefited from improved models of visual perception. However, over the last decade, research in image quality has largely shifted from the previous broader objective of gaining a better understanding of human vision, to the current limited objective of better fitting the available ground-truth data. In this paper, we discuss seven open challenges in image quality research. These challenges stem from lack of complete perceptual models for: natural images; suprathreshold distortions; interactions between distortions and images; images containing multiple and nontraditional distortions; and images containing enhancements. We also discuss challenges related to computational efficiency. The objective of this paper is not only to highlight the limitations in our current knowledge of image quality, but to also emphasize the need for additional fundamental research in quality perception.

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