A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

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 propose a new measure of perceptual image quality based on a multiple channel human visual system (HVS) model for use in digital image compression. The model incorporates the HVS light sensitivity, spatial frequency and orientation sensitivity, and masking effects. The model is based on the concept of local band-limited contrast (LBC) in oriented spatial frequency bands. This concept leads to a simple masking function. The model has the flexibility to account for the changes in frequency sensitivity as a function of local luminance and is consistent with masking experiments using gratings and edges. Numerical scaling experiments with a test panel and a set a test images that were coded using different coding algorithms showed that the proposed measure correlates better with perceptual image quality than the conventional SNR measure. 1. INTRODUCTION In optimization and evaluation of digital image compression algorithms, the signal to noise ratio (SNR) is generally used as a measure...

Abstract—We model a degraded image as an original image that has been subject to linear frequency distortion and additive noise injection. Since the psychovisual effects of frequency distortion and noise injection are independent, we decouple these two sources of degradation and measure their effect on the human visual system. We develop a distortion measure (DM) of the effect of frequency distortion, and a noise quality measure (NQM) of the effect of additive noise. The NQM, which is based on Peli’s contrast pyramid, takes into account the following: 1) variation in contrast sensitivity with distance, image dimensions, and spatial frequency; 2) variation in the local luminance mean; 3) contrast interaction between spatial frequencies; 4) contrast masking effects. For additive noise, we demonstrate that the nonlinear NQM is a better measure of visual quality than peak signal-to-noise ratio (PSNR) and linear quality measures. We compute the DM in three steps. First, we find the frequency distortion in the degraded image. Second, we compute the deviation of this frequency distortion from an allpass response of unity gain (no distortion). Finally, we weight the deviation by a model of the frequency response of the human visual system and integrate over the visible frequencies. We demonstrate how to decouple distortion and additive noise degradation in a practical image restoration system. Index Terms—Computational vision, human visual system modeling, image quality. I.

This thesis presents a generic and principled solution for optimising the visual complexity of any arbitrary computer-generated virtual environment (VE). This is performed with the ultimate goal of reducing the inherent latencies of current virtual reality (VR) technology. Effectively, we wish to remove extraneous detail from an environment which the user cannot perceive, and thus modulate the graphical complexity of a VE with little or no perceptual artifacts. The work proceeds by investigating contemporary models and theories of visual perception and then applying these to the field of real-time computer graphics. Subsequently, a technique is devised to assess the perceptual content of a computer-generated image in terms of spatial frequency (c/deg), and a model of contrast sensitivity is formulated to describe a user's ability to perceive detail under various conditions in terms of this metric. This allows us to base the level of detail (LOD) of each object in a VE on a measure of ...

High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multiscale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of “companding”, in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.

In this paper we introduce a new perceptual metric for efficient, high quality, global illumination rendering. The metric is based on a rendering-by-components framework in which the direct, and indirect diffuse, glossy, and specular light transport paths are separately computed and then composited to produce an image. The metric predicts the perceptual importances of the computationally expensive indirect illumination components with respect to image quality. To develop the metric we conducted a series of psychophysical experiments in which we measured and modeled the perceptual importances of the components. An important property of this new metric is that it predicts component importances from inexpensive estimates of the reflectance properties of a scene, and therefore adds negligible overhead to the rendering process. This perceptual metric should enable the development of an important new class of efficient global-illumination rendering systems that can intelligently allocate limited computational resources, to provide high quality images at interactive rates.

This paper presents a Concurrent Brightness and Contrast Scaling (CBCS) technique for a cold cathode fluorescent lamp (CCFL) backlit TFT-LCD display. The proposed technique aims at conserving power by reducing the backlight illumination while retaining the image fidelity through preservation of the image contrast. First, we explain how CCFL works and show how to model the non-linearity between its backlight illumination and power consumption. Next, we propose the contrast distortion metric to quantify the image quality loss after backlight scaling. Finally, we formulate and optimally solve the CBCS optimization problem with the objective of minimizing the fidelity and power metrics. Experimental results show that an average of 3.7X power saving can be achieved with only 10% of contrast distortion.

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The human visual system has a remarkable ability to interpret smooth patterns of light and shade on a surface in terms of 3-D surface geometry. Classical studies of shape-from-shading have assumed that surface luminance depends on the local surface orientation. This classical shading model holds, for example, on a sunny day. A common situation in which the classical model fails to hold, however, is a diffuse lighting condition such as on a cloudy day. Here we report on the first set of psychophysical experiments that explicitly address perception of shape-from-shading under diffuse lighting. Our main findings are that depth discrimination under di#use lighting is superior to that predicted by a classical sunnyday model, and by a model in which depth varies with perceived luminance i.e. dark means deep . We find that performance is correlated with the latter model, however, under both diffuse source and point source lighting. The results imply that the visual system uses multiple models when...