For the archiving of mammograms, the FDA requires lossless image compression. Using near-lossless compressors for temporary examination of medical images could be advantageous “if the interpreting physician deems that acceptable”. However, the FDA [1] states, “we do not believe there is consensus on what loss of information is acceptable”. To address this concern, we present a new near-lossless compression algorithm for grayscale medical images that guarantees a minimum PSNR value.

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows-Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique [1], [2]. During the compression process, we encrypt the frequency vector of the Inversion Ranking transformation and transmit it along with the compressed data. Since the frequency vector is required for decompression, no further encryption is necessary to secure the compressed file, see Figure 1.

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.

DNA microarray images are used to identify and monitor gene activity, or expression. In this study, we investigate the performance of the inversion coding technique in lossless compression of DNA microarray images. We show that inversion coding outperforms commonly used entropy coders and generic image compressors.

In this work, we propose a new method, a modified pseudo-distance technique, for color-mapped image compression. There are several techniques that yield better compression results than GIF and PNG; however, some algorithms require two passes on the image data, while some do not run in linear time. Unlike these methods, the pseudo-distance technique requires one pass and runs in linear time.