Lossy compression algorithms used in digital video systems produce artifacts whose visibility strongly depends on the actual image content. Simple error measures such as RMSE or PSNR, albeit popular, ignore this important fact and are only a mediocre predictor of perceived quality. Many applications require more reliable assessment methods. This paper discusses issues in vision modeling for perceptual video quality assessment (PVQA). Its purpose is not to describe a particular model or system, but rather to summarize and to provide pointers to up-to-date knowledge of important characteristics of the human visual system, to explain how these characteristics may be incorporated in vision models for PVQA, to give a brief overview of the state-of-the-art and current efforts in this field, and to outline directions for future research.

We present an objective image quality assessment technique which is based on the properties of the human visual system (HVS). It consists of two major components: an early vision model (multi-channel and designed specifically for complex natural images), and a visual attention model which indicates regions of interest in a scene through the use of Importance Maps. Visible errors are then weighted, depending on the perceptual importance of the region in which they occur. We show that this technique produces a high correlation with subjective test data (0.93), compared to only 0.65 for PSNR. This technique is particularly useful for images coded with spatially varying quality.

Color perception depends profoundly on adaptation processes that adjust sensitivity in response to the prevailing pattern of stimulation. We examined how color sensitivity and appearance might be influenced by adaptation to the color distributions characteristic of natural images. Color distributions were measured for natural scenes by sampling an array of locations within each scene with a spectroradiometer, or by recording each scene with a digital camera successively through 31 interference filters. The images were used to reconstruct the L, M and S cone excitation at each spatial location, and the contrasts along three post-receptoral axes [L + M, L- M or S- (L + M)]. Individual scenes varied substantially in their mean chromaticity and luminance, in the principal color-luminance axes of their distributions, and in the range of contrasts in their distributions. Chromatic contrasts were biased along a relatively narrow range of bluish to yellowish-green angles, lying roughly between the S- (L + M) axis (which was more characteristic of scenes with lush vegetation and little sky) and a unique blue-yellow axis (which was more typical of arid scenes). For many scenes L- M and S- (L + M) signals were highly correlated, with weaker correlations between luminance and chromaticity. We use a two-stage model (von Kries scaling followed by decorrelation) to show how the appearance of colors may be altered by light adaptation to the mean of the distributions and by contrast adaptation to the contrast range and principal axes of the distributions; and we show that such adjustments are qualitatively consistent with empirical measurements of asymmetric color matches obtained after adaptation to successive random samples drawn from natural distributions of chromaticities and lightnesses. Such adaptation effects define the natural range of operating states of the visual system. © 1997 Elsevier Science Ltd Color vision Color appearance Contrast adaptation Natural images Light adaptation Chromatic adaptation

Visually lossless image compression algorithms aim to keep the compression-induced distortions below the threshold of visual detection, most-often by exploiting the fact that contrast sensitivity varies with spatial frequency. However, when an image is coded in a visually lossy manner, there is little evidence to suggest that visual quality is preserved by minimizing the compression-induced distortions. This paper presents a visually lossy wavelet image compression algorithm based on contrast allocations and visual global precedence: subbands are quantized such that the distortions in the reconstructed image exhibit specific root-mean squared contrast ratios, and such that edge structure is preserved across scale-space, with a preference for global spatial scales. A model which relates contrast (of the distortions) in the reconstructed image to mean-squared error in the wavelet subbands is derived and presented; this model provides an e#cient means of adjusting contrast in the transform domain via traditional quantization techniques, thus allowing the algorithm to be used in a wide variety of coders.

We are interested in metrics for automatically predicting the compression settings for stereoscopic images so that we can minimize file size, but still maintain an acceptable level of image quality. Initially we investigate how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly coded stereoscopic image pairs. Our results suggest that symmetric, as opposed to asymmetric stereo image compression, will produce significantly better results. However, PSNR measures of image quality are widely criticized for correlating poorly with perceived visual quality. We therefore consider computational models of the Human Visual System (HVS) and describe the design and implementation of a new stereoscopic image quality metric. This point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes in regions of high spatial frequency, based on Michelson’s Formula and Peli’s Band Limited Contrast Algorithm. To establish a baseline for comparing our new metric with PSNR we ran a trial measuring stereoscopic image encoding quality with human subjects, using the Double Stimulus Continuous Quality Scale (DSCQS) from the ITU-R BT.500-11 recommendation. The results suggest that our new metric is a better predictor of human image quality preference than PSNR and could be used to predict a threshold compression level for stereoscopic image pairs.

The statistics of natural images have attracted the attention of researchers in a variety of fields and have been used as a means to better understand the human visual system and its processes. A number of algorithms in computer graphics, vision and image processing take advantage of such statistical findings to create visually more plausible results. With this report we aim to review the state of the art in image statistics and discuss existing and potential applications within computer graphics and related areas.

www.elsevier.com/locate/visres Anisotropic local contrast normalization: The role of stimulus orientation and spatial frequency bandwidths in the oblique and horizontal effect perceptual anisotropies

The statistics of natural images have attracted the attention of researchers in a variety of fields and have been used as a means to better understand the human visual system and its processes. A number of algorithms in computer graphics, vision and image processing take advantage of such statistical findings to create visually more plausible results. With this report we aim to review the state of the art in image statistics and discuss existing and potential applications within computer graphics and related areas. 1.