A technique of partial subtractive dither has been developed to improve the performance of any of a variety of near-lossless data-compression algorithms. The technique may be applicable to compression of scientific and medical image data.
To those skilled in the art of data compression, the use of subtractive dither to reduce the undesired quantization artifacts produced by lossy data-compression algorithms is well known. These artifacts include (1) biased average signal values in some regions of a signal, (2) steplike signal-value profiles in slowly changing portions of the signal, and (3) erasure of faint features that, if they occupied a sufficiently large area, would be detectable in the original signal.
The type of subtractive dither used heretofore, called "standard subtractive dither," involves a dither distribution that is uniform over a range equal to the quantization step size of the algorithm to which it is applied. Standard subtractive dither incurs costs in the form of an increase in rate (in other words, a reduction in compression) and an increase in distortion.
In the present technique of partial subtractive dither, the dither distribution is uniform over a range smaller than the quantization step size. The choice of a uniform distribution is motivated by a desire to optimally compromise between the benefit (reduction in quantization artifacts) and the costs (increases in rate and distortion) of standard subtractive dither. Under some reasonable assumptions, the dither distribution should be chosen to be uniform, with its range chosen according to the degree of dithering desired.
The figure shows the effects of compression followed by decompression of an image. As the degree of dithering increases from zero (no dither) through partial to standard, the appearance of streaks and artificial regions of constant intensity decreases, while the overall grainy appearance increases.
This work was done by Matthew Klimesh of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.nasatech.com/tsp under the Information Sciences category.
NPO-20964
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Partial Subtractive Dither for Lossy Data Compression
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Overview
The document discusses a technique known as partial subtractive dither, which aims to enhance the performance of various near-lossless data-compression algorithms, particularly in the context of scientific and medical image data. The primary goal of this technique is to mitigate the quantization artifacts that often arise from lossy data-compression methods. These artifacts can manifest as biased average signal values, steplike profiles in slowly changing signal areas, and the erasure of faint features in the original signal.
The document outlines the mathematical foundations of optimal dither distributions, indicating that uniform distributions on specific intervals can provide the best trade-offs between distortion and compression efficiency. The results suggest that the optimal dither distributions are uniform on the interval [-k/2, k/2], where k varies between 0 and a specified maximum value q. The implications of these findings are significant for applications requiring high fidelity in image reconstruction, as they allow for a controlled introduction of noise that can improve the overall quality of the compressed image.
Several theorems are presented to support the claims regarding optimal dither distributions, including a discrete case where the samples are integers. The document emphasizes the need for experimentation and subjective judgment to determine the best compromise between the degree of artifacts and the costs associated with dithering.
An example is provided, illustrating the application of these results to an image referred to as "munar," which was compressed using a predictive algorithm. The study demonstrates how varying the amplitude of the dither signal affects the appearance of the reconstructed image, highlighting the importance of dither in achieving near-lossless compression. The findings indicate that while the original and reconstructed images may appear indistinguishable under normal display conditions, the differences become relevant in scientific analysis contexts.
In conclusion, the document emphasizes the potential of partial subtractive dither to improve data compression techniques, particularly in fields where image quality is paramount. It suggests future research directions, including the exploration of dither applications in non-uniform quantization scenarios, which could further enhance compression performance under specific conditions.
