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In the perceptually transparent coding of images, we use representation and quantization strategies that exploit properties of human perception to obtain an approximate digital image indistinguishable from the original. This image is then encoded in an error free manner. The resulting coders have better performance than error free coding for a comparable quality. Further, by considering changes to images that do not produce perceptible distortion, we identify image characteristics onerous for the encoder, but perceptually unimportant. Once such characteristic is the typical noise level, often imperceptible, encountered in still images. Thus, we consider adaptive noise removal to improve coder performance, without perceptible degradation of quality. In this paper, several elements contribute to coding efficiency while preserving image quality: adaptive noise removal, additive decomposition of the image with a high activity remainder, coarse quantization of the remainder, progressive representation of the remainder, using bilinear or directional interpolation methods, and efficient encoding of the sparse remainder. The overall coding performance improvement due to noise removal and the use of a progressive code is about 18%, as compared to our previous results for perceptually transparent coders. The compression ratio for a set of nine test images is 3.72 for no perceptible loss of quality.

A number of new approaches to image coding are being developed to meet the increasing need for a broader range of quality and usage environments for image compression. Several of these new approaches, such as subband coders, are intended to provide higher quality images at the same bit rate as compared to the JPEG standard, because they are not subject to end of block artifacts, or because they are inherently better attuned to the image representation that occurs in the peripheral visual system. Still, in the absence of a pertinent quality criterion, the quality and performance of subband coders, or wavelet coders, can be mediocre. We have developed over the past few years the elements of a methodology applicable to this problem. We reported last year at the SPIE in San Diego, a Comparison of Coding Techniques based on a new Picture Quality Scale (PQS). In that work, we were able to rate coders designed by any criteria, on the basis of performance and quality. The problem that we are now considering is to design the coding technique so as to provide a better quality or a lower bit rate. The image quality, as evaluated by PQS, depends on a combination of several objective distortion factors, which can be identified with perceived coding artifacts, but the design of coders using all the factors is much too complex for an analytical approach. We make use instead of two design methodologies. The first one is to optimize the design of an existing subband coder using PQS as a distortion metric. The second one makes use of a methodology for the design of linear filters based on properties of human perception that we have developed previously and that may provide a tractable design method.