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-neural- network 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]. Further- more, 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.
Compression has enabled years of exponential growth in global video consumption, providing video everywhere, with few perceptible artifacts. Automated Video Quality Assessment (VQA) is an enabler of compression. We present data showing video contrast affects on artifact visibility. Based on our data, we propose a contrast-gain-control VQA algorithm, with target spatiotemporal property weighting, and using our data to tune existing VQA algorithms for improved artifact threshold predictions. This paper provides much needed data on natural video mask contrast and artifact visibility, and provides important insights for how VQA algorithms can be improved to better predict video quality in the high-quality regime.
Perceptual quality assessment of digital images and videos are important for various image-processing applications.
For assessing the image quality, researchers have often used the idea of visual masking (or distortion
visibility) to design image-quality predictors specifically for the near-threshold distortions. However, it is still
unknown that while assessing the quality of natural images, how the local distortion visibilities relate with the
local quality scores. Furthermore, the summing mechanism of the local quality scores to predict the global quality
scores is also crucial for better prediction of the perceptual image quality. In this paper, the local and global
qualities of six images and six distortion levels were measured using subjective experiments. Gabor-noise target
was used as distortion in the quality-assessment experiments to be consistent with our previous study [Alam,
Vilankar, Field, and Chandler, Journal of Vision, 2014], in which the local root-mean-square contrast detection
thresholds of detecting the Gabor-noise target were measured at each spatial location of the undistorted images.
Comparison of the results of this quality-assessment experiment and the previous detection experiment shows
that masking predicted the local quality scores more than 95% correctly above 15 dB threshold within 5% subject
scores. Furthermore, it was found that an approximate squared summation of local-quality scores predicted the
global quality scores suitably (Spearman rank-order correlation 0:97).
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.
The ability of an image region to hide or mask a target signal continues to play a key role in the design of numerous image-processing and vision applications. However, one of the challenges in designing an effective model of masking for natural images is the lack of ground-truth data. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds (masking maps) for a database of natural images. Via a three-alternative forced-choice experiment, we measured the thresholds for detecting 3.7 cycles/deg vertically oriented log-Gabor targets placed within each 85×85-pixel patch (1.9 deg patch) of 15 natural images from the CSIQ image database [Larson and Chandler, JEI, 2010]. Thus, for each image, we obtained a masking map in which each entry in the map denotes the RMS contrast threshold for detecting the log-Gabor target at the corresponding spatial location in the image. Here, we describe the psychophysical procedures used to collect the thresholds, we provide analyses of the results, and we provide some outcomes of predicting the thresholds via basic low-level features, a computational masking model, and two modern imagequality assessment algorithms.
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