Simple bit-plane coding for lossless image compression and extended functionalities

This work introduces a new technique for lossless compression of color images. The technique is composed of first transforming an RGB image into luminance and chrominance domain (Y CuCv). Then, the luminance channel Y is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced by Kim and Cho, Burrows-Wheeler Inversion Coder (BWIC) or JPEG 2000 is used to compress of the chrominance channels Cu and Cv. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes such as CALIC, JPEG 2000, LOCO-I, BPG(HEVC), and the previously proposed hierarchical prediction and context adaptive coding technique LCIC.

This paper is proposing a novel approach for lossless compression of both gray scale and color images. This approach is exploiting the fact that neighboring pixel values of most of the real world images do not change drastically. A code book of 256 × 256 known at both the compressing and decompressing stations is used as reference for the proposed lossless compression algorithm where a compression ratio dependent on code book design concepts is realized. This code book design is considered in an innovative way so as to further minimize the compression ratio in multiple iterations. Two types of code book designs are discussed in the paper. Level of compression, compressed image and the code book identification number shall be send to the receiver who decompresses the image based on the level and code books available. This method is highly useful for the exact type of images when the code books are designed specifically for them. Simulation results are explained to show how compression ratio and compression speed are improved in both the present method as compared to conventional methods.

Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000

[Summary form only given] Typically, the lossless compression of color images is achieved by separately compressing the three RGB monochromatic image components. The proposed method takes into account the fact that high spatial correlations exist not only within each monochromatic frame but also between similar spatial locations in adjacent monochromatic frames. Based on the observation that the prediction errors produced by the JPEG predictor in each RGB monochromatic frame present very similar structures, we propose two new chromatic predictors, called chromatic differential predictor (CDP) and classified CDP (CCDP), to capture the spectral dependencies between the monochromatic frames. In addition to prediction schemes, we consider context modeling schemes that take into account the prediction errors in spatially and/or spectrally adjacent pixels in order to efficiently encode the prediction errors. In order to demonstrate the advantage of the proposed lossless color image compression scheme, 5 different types of images are selected from the KODAK image set. All images are RGB 24 bpp color images with resolution 768/spl times/512. The experimental results demonstrate significant improvement in compression performance. Its fast implementation and high compression ratio may be a promising approach for the application of real-time color video compression.

Lossless gray scale image compression is necessary for many purposes, such as medical imaging, image databases and so on. Lossy images are important as well, because of the high compression ratio. The authors propose a lossless image compression scheme using a lossy image generated with the JPEG-DCT scheme. The concept is, send a JPEG-compressed lossy image primarily, then send residual information and reconstruct the original image using both the lossy image and residual information. 3D adaptive prediction and adaptive arithmetic coding are used, which fully use the statistical parameters of the distribution of the symbol source. The optimal number of neighbor pixels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the original lossless algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this article we report on a study of how to use the context tree weighting (CTW) algorithm for lossless image compression. This algorithm has been shown to perform optimally, in terms of redundancy, for a wide class of data sources. Our study shows that this algorithm can successfully be applied to image compression even in its basic form. We also report on possible modifications of the basic CTW algorithm to let it work more efficiently for image data. Our research is briefly focussed on the compression of medical gray scale images.

Lossless image compression and encryption using SCAN

To assess retrospectively the effect of the Joint Photographic Experts Group 2000 (JPEG2000) compression ratio on the quality of thin-section computed tomographic (CT) images. In this institutional review board-approved investigation (protocol 238/2004), thin-section CT images were subjected to irreversible JPEG2000 compression by using five compression ratios (3:1, 5:1, 7:1, 9:1, and 11:1). Three radiologists independently evaluated 60 thin-section CT images, of various diseases, that were obtained with single-detector (weighted dose index, 14.4 mGy) and multidetector (weighted dose index, 9.8 mGy) CT. Toggling between the original and compressed images, readers had to identify the original image by using a forced-choice two-alternative model and to subjectively rank the quality of what they believed to be the compressed image. To assess the reader's ability to distinguish the compressed from the original image, a binomial test was used. Bonferroni correction was applied for all multiple tests. Images compressed with a ratio of 3:1 were not distinguishable from original images (P > .2 for all readers). With use of the 5:1 ratio, minor differences in appearance between the compressed and original images were seen by one of the three readers. With use of higher compression ratios (>/=7:1), all readers (P < .001) recognized the original image. The quality of more than 90% of the images compressed with a 7:1 or higher ratio was substantially degraded. Single-detector and multidetector CT results were not significantly different. The highest ratio that yielded visually lossless compression of thin-section CT images was 3:1. With the 5:1 ratio, there was minor image quality loss, while use of higher compression ratios (>/=7:1) caused substantial degradation of image quality and potential loss of diagnostic information.

Medical images usually require higher fidelity than commonly used natural images, especially with respect to detail preservation. Color medical images also require a higher degree of color preservation. In this paper, we present the extension of the novel gray-scale image compression technique, hybrid multi-scale vector quantization (HMVQ) to color image compression. Limitations of common color image compression methods in controlling bit-rate allocation for color channels are discussed, and the ability of HMVQ in overcoming such limitations while improving the color quality is demonstrated.

Image compression and progressive transmission (PIT) is a technique used as an alternative solution to the communication problem, where there is a need to transmit huge data such as in medical image transmission. The technique divides image that is to be transmitted into several phases and effectively provides a fairly accurate reconstruction of the original image in every phase. This paper proposes a novel architecture that combines bit-plane slicing, DD-dual-tree DWT, and SPIHT. The bit-plane slicing separates the image into eight planes; each bit plane is then decomposed with wavelet. The wavelet coefficients in different sub-bands are then encoded using SPIHT and transmitted progressively and reconstructed by combining the data received in each phase with the previous phase data. Experimental results have shown that the proposed technique has better efficiency. Even at the lowest SPIHT rate of 0.1 bits/pixel for a 512 × 512 gray scale medical image, the reconstructed image shows higher quality at third phase itself, which makes it an efficient choice for transmission of medical images even at low speed communication channel.

Three kinds of data structures related to the quadtree and the octree are reviewed, and improvements, particularly far the storage requirements, are introduced. Applications to geology are indicated where possible. First, the bitwise condensed quadtree (BC quadtree) and the gray scale depth first expression are presented, which lead to a better manipulation and compression of gray scale images and density data. The BC quadtree results from a combination of spatial hierarchical subdivision with gray scale hierarchical subdivision and can easily be adapted to the representation of imprecise information. Second, examining the node types of the polytree, compaction is improved by defining some new node types. The polytree is a variant of the octree used in solid modeling to represent polygonal and polyhedral data. A special property of the new node types is that they can easily be processed with the conventional algorithms. Third, the storage of different kinds of very large quadtrees on external storage media leads to the desire for an unified way of access. Until now, the linear quadtree was the only data structure far this task. Its storage requirements are considerably improved by some simple changes.

The FBI has developed a specification for the compression of gray-scale fingerprint images to support paperless identification services within the criminal justice community. The algorithm is based on a scalar quantization of a discrete wavelet transform decomposition of the images, followed by zero run encoding and Huffman encoding.

In this paper we propose a DWT-based perceptually lossless image compression algorithm and architecture. Many image-capturing devices acquire images in subsampled R-G-B format and have real-time constraints which prohibit conversion to other formats, generally preferred for image compression, such as Y-Cb-Cr. Therefore, we propose a compression algorithm which works performed directly on the subsampled R-G-B data. The scheme exploits the correlation between the color planes and the perceptual sensitivity of the human visual system in order to improve the compression ratio. Moreover it can be easily mapped into an integrated fast architecture yielding very high throughput.

In this paper, we propose a new method based on multiple-valued functions and spectral analysis for the lossless compression of binary, gray scale and color biomedical images. Preprocessing of the raw image data is first applied to make the data more amenable for compression. The preprocessed image is then segmented into blocks to which various coding and compression is applied. Detailed description of segmentation and compression schemes is presented. The proposed scheme has been implemented in the C language and compared with other state-of-the-art techniques. Experimental results indicate significant improvements in compression ratios.

In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5% over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3% compression gain over our previous work.