New and Specialized Methods of Image Compression.

Due to the enormous amounts of images produced today, compression is crucial for consumer and professional (for instance, medical) picture archiving and communication systems [...

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images.

The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US).

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform.

A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation.

Skipping Selected Steps of DWT Computation in Lossless JPEG 2000 for Improved Bitrates.

In order to improve bitrates of lossless JPEG 2000, we propose to modify the discrete wavelet transform (DWT) by skipping selected steps of its computation. We employ a heuristic to construct the skipped steps DWT (SS-DWT) in an image-adaptive way and define fixed SS-DWT variants. For a large and diverse set of images, we find that SS-DWT significantly improves bitrates of non-photographic images.

Application of detector precision characteristics and histogram packing for compression of biological fluorescence micrographs.

Modern applications of biological microscopy such as high-content screening (HCS), 4D imaging, and multispectral imaging may involve collection of thousands of images in every experiment making efficient image-compression techniques necessary. Reversible compression algorithms, when used with biological micrographs, provide only a moderate compression ratio, while irreversible techniques obtain better ratios at the cost of removing some information from images and introducing artifacts. We construct a model of noise, which is a function of signal in the imaging system.

A comparison of two methods for retrieval of medical images in the compressed domain.

Retrieval of images based on features derived directly from the images provides a useful way of accessing medical imagery. While most of these techniques operate in the pixel domain, a more efficient approach is to perform retrieval directly in the compressed domain of images. In this paper we look at two different methods for such compressed-domain retrieval of medical images.

An evaluation of lossless compression algorithms for medical infrared images.

Several popular lossless image compression algorithms were evaluated for the application of compressing medical infrared images. Lossless JPEG, JPEG-LS,JPEG2000, PNG, and CALIC were tested on an image dataset of 380+ thermal images. The results show that JPEG-LS is the algorithm with the best performance, both in terms of compression ratio and compression speed.