We present a study into the perception of display brightness as related to the physical size and distance of the screen from the observer. Brightness perception is a complex topic, which is influenced by a number of lower- and higher-order factors—with empirical evidence from the cinema industry suggesting that display size may play a significant role. To test this hypothesis, we conducted a series of user studies exploring brightness perception for a range of displays and distances from the observer that span representative use scenarios. Our results suggest that retinal size is not sufficient to explain the range of discovered brightness variations, but is sufficient in combination with physical distance from the observer. The resulting model can be used as a step toward perceptually correcting image brightness perception based on target display parameters. This can be leveraged for energy management and the preservation of artistic intent. A pilot study suggests that adaptation luminance is an additional factor for the magnitude of the effect.

Abstract Measuring the spectral power distribution of a light source, that is, the emission as a function of wavelength, typically requires the use of spectrophotometers or multi‐spectral cameras. Here, we propose a low‐cost system that enables the recovery of the visible light spectral signature of different types of light sources without requiring highly complex or specialized equipment and using just off‐the‐shelf, widely available components. To do this, a standard Digital Single‐Lens Reflex (DSLR) camera and a diffraction filter are used, sacrificing the spatial dimension for spectral resolution. We present here the image formation model and the calibration process necessary to recover the spectrum, including spectral calibration and amplitude recovery. We also assess the robustness of our method and perform a detailed analysis exploring the parameters influencing its accuracy. Further, we show applications of the system in image processing and rendering.

Color coding efficacy is critical to the success of any image compression method. The initial quantization step that happens during encoding limits the accuracy and directly affects compression performance. If too many code words are spent in areas of the color space that make little difference to perception, compression is taxed. If other areas are under-sampled, contour (banding) and color shifting artifacts can result. Legacy color encodings such as 24-bit sRGB do not easily extend to wide color gamuts and high dynamic range due to perceptual non-uniformity issues. In this paper, we compare a number of different color encodings and bit depths for suitability in HDR and wide gamut applications using a new metric for evaluating encoding efficacy, Number of Distinguishable Colors.

Managing the appearance of images across different display environments is a difficult problem, exacerbated by the proliferation of high dynamic range imaging technologies. Tone reproduction is often limited to luminance adjustment and is rarely calibrated against psychophysical data, while color appearance modeling addresses color reproduction in a calibrated manner, albeit over a limited luminance range. Only a few image appearance models bridge the gap, borrowing ideas from both areas. Our take on scene reproduction reduces computational complexity with respect to the state-of-the-art, and adds a spatially varying model of lightness perception. The predictive capabilities of the model are validated against all psychophysical data known to us, and visual comparisons show accurate and robust reproduction for challenging high dynamic range scenes.