Evaluation of Algorithms for Lossless Compression of Continuous-Tone Images
Andreas E. Savakis Department of Computer Engineering Rochester Institute of Technology Rochester, New York 14623
[email protected]
Evaluation of Algorithms for Lossless Compression of Continuous-Tone Images
Andreas E. Savakis Department of Computer Engineering Rochester Institute of Technology Rochester, New York 14623
[email protected]
Abstract Lossless image compression algorithms for continuous-tone images have received a great deal of attention in recent years. However, reports on benchmarking their performance have been limited. In this paper, we present a comparative study of the following algorithms: UNIX compress, gzip, LZW, Group 3, Group 4, JBIG, old lossless JPEG, JPEG-LS based on LOCO, CALIC, FELICS, S+P Transform, and PNG. The test images consist of two sets of eight bits/pixel continuous-tone images: one set contains nine pictorial images, and another set contains eight document images, obtained from the standard set of CCITT images that were scanned and printed using eight bits/pixel at 200 dpi. In cases where the algorithm under consideration could only be applied to binary data, the bitplanes of the gray scale image were decomposed, with and without Gray encoding, and the compression was applied to individual bit planes. The results show that the best compression is obtained using the CALIC and JPEG-LS algorithms.
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1. Introduction The objective of lossless image compression is to generate the lowest possible bit-rate representation of an image, while maintaining the capability to fully recover the original image data after decompression.1-3 An issue of practical importance is the computational complexity and time required for compression and decompression. Excessive complexity, or execution time, requirements render certain approaches impractical, even if they can achieve good compression. The requirement of full data recovery requires that all the operations that are applied to the image should be reversible. This is what differentiates lossless from lossy compression methods, such as the JPEG baseline algorithm,4-5 that employ quantization, and sacrifice full-image recovery to improve on the bit-rate associated with the compressed image. A tradeoff between lossy and lossless compression is found in nearly lossless compression algorithms, which introduce minor errors that are not visually perceptible in the decompressed image in order to improve compression performance. The objective of this work is the benchmarking of strictly lossless compression methods. For a discussion of nearly lossless image compression algorithms the interested reader is referred to the work of Ansari et.al.6 Document images that include text and graphics are typically represented using bitonal data. Such images are commonly encountered in fax transmission and high-speed production scanning, and have prompted the development of document image processing and compression algorithms that deal exclusively with bitonal data. Standards for lossless compression of binary images, such as CCITT Group 3, Group 4, and JBIG,3,7 are widely used in practice for fax transmission and document image storage, and will be discussed in Section 2.1. Lossless compression algorithms for gray scale images have received increasing attention in the past decade.8-10 Initially, their development was motivated by the requirement to process images with sensitive content where artifacts could not be tolerated, such as medical, satellite, and astronomical images.11-16 In recent years, new image processing applications have emerged that require lossless compression of
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continuous-tone pictorial and document images. For example, watermarks are embedded in images for copyright, authentication, or security purposes. 17-20 While watermarks are designed to be robust to lossy compression, authentication methods detect even the smallest changes in image content resulting from lossy compression, and may require the use of lossless compression. Despite the proliferation of lossless compression algorithms for pictorial images, there have been few efforts to evaluate their performance when processing gray scale document images. The objective of this work is to provide a comparative assessment of several state-of-the-art lossless compression algorithms in the framework of both continuous-tone pictorial and document images. Pictorial and document images constitute the vast majority of images used by consumers and businesses, and are the most likely to require the use of watermarking for security and authentication. In addition, these two types of images are often combined to generate compound document images, i.e., images that include text, graphics and pictures. Compound images are becoming more widespread, with examples including insurance claims with pictures attached, applications where the applicant’s picture is included, etc. Practical constraints make it difficult to implement all of the existing lossless compression algorithms and test them for all possible image types. As a result, the scope of this effort is limited to the comparison of several widely used methods. This limitation is partly offset by the fact that the standard algorithms for lossless compression of bitonal and gray scale images are included in this study, and a diverse sample of images is used. Two data sets are considered: one containing gray scale pictorial images and one containing gray scale document images. The results can be extrapolated to lossless compression of color images and compound documents. In the context of color image processing, it is possible to assume that individual channels of the color image are processed independently as if they were gray scale images. An argument can be made that there is correlation between the color bands that should be exploited when designing algorithms for color image processing. A simple way to address this issue is to decorrelate the color channels during preprocessing, with a color transformation such as RGB to YC b C r . It should be
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noted that invertible color transforms have been designed and are under consideration for use in the lossless mode of the emerging JPEG2000 standard. Invertible transforms are based on integer approximations of floating point transforms, so that the RGB color data can be fully recovered after the inverse transformation is performed. This type of processing is useful, but is not considered in this work, since we are dealing exclusively with gray scale images.
2. General Approach to Lossless Compression Lossless compression methods may be categorized into sequential, where the image pixels are typically visited in a raster scan fashion, and transform, where multiresolution decomposition of the image is used to obtain a hierarchical description of the image and allow for progressive transmission.10 The problem of lossless compression involves the general steps of data modeling and data encoding.21-22 The modeling part of the system is designed to provide an estimate of the probability of each symbol at the time of coding. If we assume that the image is scanned in a raster fashion, then the conditional probability distribution of each pixel x(i) is expressed as: q{x(i)} = p{x(i) | x(i-1), x(i-2), … x(1), x(0)}, 0
