Visual communication with retinex coding.

Visual communication with retinex coding seeks to suppress the spatial variation of the irradiance (e.g., shadows) across natural scenes and preserve only the spatial detail and the reflectance (or the lightness) of the surface itself. The separation of reflectance from irradiance begins with nonlinear retinex coding that sharply and clearly enhances edges and preserves their contrast, and it ends with a Wiener filter that restores images from this edge and contrast information. An approximate small-signal model of image gathering with retinex coding is found to consist of the familiar difference-of-Gaussian bandpass filter and a locally adaptive automatic-gain control. A linear representation of this model is used to develop expressions within the small-signal constraint for the information rate and the theoretical minimum data rate of the retinex-coded signal and for the maximum-realizable fidelity of the images restored from this signal. Extensive computations and simulations demonstrate that predictions based on these figures of merit correlate closely with perceptual and measured performance. Hence these predictions can serve as a general guide for the design of visual communication channels that produce images with a visual quality that consistently approaches the best possible sharpness, clarity, and reflectance constancy, even for nonuniform irradiances. The suppression of shadows in the restored image is found to be constrained inherently more by the sharpness of their penumbra than by their depth.

Information metric as a design tool for optoelectronic imaging systems.

The resolution of images acquired by a digital camera is limited to the camera's sampling interval. The images' visual quality is affected by the level of the degradations caused by the imaging process from acquisition to display, including quantization, coding, transmission, and digital filtering. The information metric is presented as a design and an assessment tool for high-resolution digital imaging systems and all their subsystems. It associates gains in the acquired information with improvements in resolution, sharpness, and clarity of the final image representation. It demonstrates the need to integrate a digital filtering module that accounts for the optoelectronic imaging degradations in the optoelectronic imaging design and assessment. It further demonstrates the metric's sensitivity by the assessment of the combined imaging processes as a unified system.

Nonlinear dynamic range transformation in visual communication channels.

The article evaluates nonlinear dynamic range transformation in the context of the end-to-end continuous-input/discrete processing/continuous-display imaging process. Dynamic range transformation is required when we have the following: (i) the wide dynamic range encountered in nature is compressed into the relatively narrow dynamic range of the display, particularly for spatially varying irradiance (e.g., shadow); (ii) coarse quantization is expanded to the wider dynamic range of the display; and (iii) nonlinear tone scale transformation compensates for the correction in the camera amplifier.