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.

This paper evaluates optimal quantization and data compression in the context of the end-to-end model for 2-D sampled imaging systems. Results show that the minimum number of bits (data density) required for lossless information transmission depends on the design of the image-gathering device. The information in the acquired signal, not its energy, dictates the trade-off between data transmission and visual quality. The entropy of the encoded signal does not indicate neither the amount of information conveyed by the process nor the preferable design tradeoffs for sampled imaging systems. Optimal end-to-end system design for constraint transmission inevitably involves a trade-off between electronic noise and quantization error. The resulting end-to-end design minimizes the loss of information and maximizes the efficiency of its transfer.>

A novel imaging process consisting of multiresponse imaging and Wiener-matrix restoration is presented. This process acquires and reassembles A multiresponse images into a single minimum mean square restoration error (MSRE) image with a resolution that is square root A times finer than the photodetector-array sampling lattice.>

An algorithm is developed for defining small kernels that are conditioned on the important components of the imaging process: the nature of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and post-filter interpolation. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernal values that minimize the expected mean-square error of the estimate of the scene characteristic. This development is consistent with the derivation of the spatially unconstrained Wiener characteristic filter, but leads to a small, spatially constrained convolution kernel. Simulation experiments demonstrate that the algorithm is more flexible than traditional small-kernel techniques and yields more accurate estimates.>

The visual system is most sensitive to structure. Moreover, many other attributes of the scene are preserved in the retinal response to edges. In this paper we present a new coding process that is based on models suggested for retinal processing in human vision. This process extracts and codes only features that are preserved by the response of these filters to an edge (edge primitives). The decoded image, obtained by recovering the intensity levels between the outlined boundaries, using only the edge primitives, attains high structural fidelity. We demonstrate that a wide variety of images can be represented by their edge primitives with high compression ratios, typically two to three orders of magnitude, depending on the target. This method is particularly advantageous when high structural fidelity representation must be combined with high data compression.