A number of successful tone mapping operators for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range devices. They were inspired by fields as diverse as image processing, photographic practice, and modeling of the human visual systems (HVS). The variety of approaches calls for a systematic perceptual evaluation of their performance. We conduct a psychophysical experiment based on a direct comparison between the appearance of real-world scenes and HDR images of these scenes displayed on a low dynamic range monitor. In our experiment, HDR images are tone mapped by seven existing tone mapping operators. The primary interest of this psychophysical experiment is to assess the differences in how tone mapped images are perceived by human observers and to find out which attributes of image appearance account for these differences when tone mapped images are compared directly with their corresponding realworld scenes rather than with each other. The human subjects rate image naturalness, overall contrast, overall brightness, and detail reproduction in dark and bright image regions with respect to the corresponding real-world scene. The results indicate substantial differences in perception of images produced by individual tone mapping operators. We observe a clear distinction between global and local operators in favor of the latter, and we classify the tone mapping operators according to naturalness and appearance attributes. Keywords: high dynamic range (HDR) images, human visual systems (HVS), tone mapping, psychophysics, ANOVA, correlation, MANOVA, Mahalanobis distances. 1.

Many current quality evaluation models were designed to produce a single estimate of perceived quality for a video sequence coded at relatively high rates. These metrics perform a multi-channel decomposition to simulate the processes of the Human Visual System (HVS), followed by a distortion pooling stage that collapses the channels over frequency, time and space. Estimating quality at short intervals over the length of a video sequence, however, may be more useful for long video sequences than a single estimate, particularly in such applications as two pass video coding and video quality monitoring. This paper presents an objective metric designed to perform this task on video sequences coded at low bit rates. The metric implements a wavelet transform-based model of the human visual system and a method of temporal error pooling suited to continuous estimation of perceived quality. A time series distance metric based on piecewise linear representations is also introduced in order to quantify performance. The metric is evaluated on a wide range of low bit rate video content and shown to perform well in terms of the shape and overall mean of the output perceived quality waveform.

The paper presents a two-stage metric which quantifies the visual quality of images that have undergone wavelet-based compression. The first stage operates via a model of visual pattern masking, which takes as input original and distorted images, and which outputs masked contrast detection thresholds. For distortions beyond the threshold of detection, the images and the thresholds are fed into a second stage which estimates visual quality based on the distance between the distribution of assumed ideal and actual contrast signal-to-noise ratios across scale-space. Results indicate that the proposed metric yields a higher correlation with subjective-rating data than other visual quality metrics when applied to a sample of wavelet-coded images for which peak signal-to-noise ratio correlates poorly with subjective quality.

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