Advanced technology development for image gathering, coding, and processing

Three overlapping areas of research activities are presented: (1) Information theory and optimal filtering are extended to visual information acquisition and processing. The goal is to provide a comprehensive methodology for quantitatively assessing the end-to-end performance of image gathering, coding, and processing. (2) Focal-plane processing techniques and technology are developed to combine effectively image gathering with coding. The emphasis is on low-level vision processing akin to the retinal processing in human vision. (3) A breadboard adaptive image-coding system is being assembled. This system will be used to develop and evaluate a number of advanced image-coding technologies and techniques as well as research the concept of adaptive image coding

Image gathering, coding, and processing: End-to-end optimization for efficient and robust acquisition of visual information

Researchers are concerned with the end-to-end performance of image gathering, coding, and processing. The applications range from high-resolution television to vision-based robotics, wherever the resolution, efficiency and robustness of visual information acquisition and processing are critical. For the presentation at this workshop, it is convenient to divide research activities into the following two overlapping areas: The first is the development of focal-plane processing techniques and technology to effectively combine image gathering with coding, with an emphasis on low-level vision processing akin to the retinal processing in human vision. The approach includes the familiar Laplacian pyramid, the new intensity-dependent spatial summation, and parallel sensing/processing networks. Three-dimensional image gathering is attained by combining laser ranging with sensor-array imaging. The second is the rigorous extension of information theory and optimal filtering to visual information acquisition and processing. The goal is to provide a comprehensive methodology for quantitatively assessing the end-to-end performance of image gathering, coding, and processing

Image gathering and coding for digital restoration: Information efficiency and visual quality

Image gathering and coding are commonly treated as tasks separate from each other and from the digital processing used to restore and enhance the images. The goal is to develop a method that allows us to assess quantitatively the combined performance of image gathering and coding for the digital restoration of images with high visual quality. Digital restoration is often interactive because visual quality depends on perceptual rather than mathematical considerations, and these considerations vary with the target, the application, and the observer. The approach is based on the theoretical treatment of image gathering as a communication channel (J. Opt. Soc. Am. A2, 1644(1985);5,285(1988). Initial results suggest that the practical upper limit of the information contained in the acquired image data range typically from approximately 2 to 4 binary information units (bifs) per sample, depending on the design of the image-gathering system. The associated information efficiency of the transmitted data (i.e., the ratio of information over data) ranges typically from approximately 0.3 to 0.5 bif per bit without coding to approximately 0.5 to 0.9 bif per bit with lossless predictive compression and Huffman coding. The visual quality that can be attained with interactive image restoration improves perceptibly as the available information increases to approximately 3 bifs per sample. However, the perceptual improvements that can be attained with further increases in information are very subtle and depend on the target and the desired enhancement

Visual Information Processing for Television and Telerobotics

This publication is a compilation of the papers presented at the NASA conference on Visual Information Processing for Television and Telerobotics. The conference was held at the Williamsburg Hilton, Williamsburg, Virginia on May 10 to 12, 1989. The conference was sponsored jointly by NASA Offices of Aeronautics and Space Technology (OAST) and Space Science and Applications (OSSA) and the NASA Langley Research Center. The presentations were grouped into three sessions: Image Gathering, Coding, and Advanced Concepts; Systems; and Technologies. The program was organized to provide a forum in which researchers from industry, universities, and government could be brought together to discuss the state of knowledge in image gathering, coding, and processing methods

Informationally Optimized Image Gathering and Restoration

The goal of image gathering and restoration often is to produce the best possible picture in terms of fidelity, sharpness and clarity. However, this goal cannot be attained, at it has been pursued in the past, by treating image gathering and restoration as independent tasks. Instead, in a clean departure from the mores of traditional image processing, we present an approach that rigorously uses modern communication theory to optimally combine the electro-optical design of the image gathering device with the digital processing algorithm for image restoration. Extensive simulations have shown that there exists a strong correlation between the information rate that is produced by the image gathering device and the image quality with which an image can be restored. Introduction Modern visual communication channels increasingly combine image gathering and display with digital image coding and restoration (Fig. 1). So far, however, the imagegathering devices are still designed to produce t..

On The Information-Theoretic Assessment Of Visual Communication

This paper deals with the extension of information theory to the assessment of visual communication from scene to observer. The mathematical development rigorously unites the electro-optical design of image gathering and display devices with the digital processing algorithms for image coding and restoration. Results show that: ffl End-to-end system analysis closely correlates with measurable and perceptual performance characteristics, such as data rate and image quality, respectively. ffl The goal of producing the best possible image at the lowest data rate can be realized only if (a) the electro-optical design of the image-gathering device is optimized for the maximum-realizable information rate and (b) the image-restoration algorithm properly accounts for the perturbations in the visual communication channel. 1. INTRODUCTION Modern visual communication channels increasingly combine image gathering and display with digital image coding and restoration (Fig. 1). So far, however, the..