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We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate vs. distortion performance is comparable to those of the most efficient lossy compression methods.

In this paper a new image transformation fitted to reversible (lossless) image compression is presented. It uses a simple pyramid multiresolution scheme which is enhanced via predictive coding. The new transformation is similar to the subband decomposition, but it uses only integer operations. The number of bits required to represent the transformed image is kept small though careful scaling and truncations. The lossless coding compression rates are smaller than those obtained with predictive coding of equivalent complexity. It is also shown that the new transform can be effectively used, with the same coding algorithm, for both lossless and lossy compression. When used for lossy compression, its rate-distortion function is comparable to other efficient lossy compression methods.

Invertible wavelet transforms that map integers to integers are important for lossless representations. In this paper, we present an approach to build integer to integer wavelet transforms based upon the idea of factoring wavelet transforms into lifting steps. This allows the construction of an integer version of every wavelet transform. We demonstrate the use of these transforms in lossless image compression. 1 INTRODUCTION High-fidelity images generated from studio-quality video, medical images, seismic data, satellite images, and images scanned from manuscripts for preservation purposes typically demand lossless encoding. Yet, the huge datasize prohibits fast distribution of data. There is thus a need to seek encoding methods that can support storage and transmission of images at a spectrum of resolutions and encoding fidelities, from lossy to lossless, for progressive delivery and for different end-users' needs. In recent years, wavelet transforms have been successfully used for ...

We propose a new framework for highly scalable video compression, using a Lifting-based Invertible Motion Adaptive Transform (LIMAT). We use motion-compensated lifting steps to implement the temporal wavelet transform, which preserves invertibility, regardless of the motion model. By contrast, the invertibility requirement has restricted previous approaches to either block-based or global motion compensation. We show that the proposed framework effectively applies the temporal wavelet transform along a set of motion trajectories. An implementation demonstrates high coding gain from a finely embedded, scalable compressed bit-stream. Results also demonstrate the effectiveness of temporal wavelet kernels other than the simple Haar, and the benefits of complex motion modeling, using a deformable triangular mesh. These advances are either incompatible or difficult to achieve with previously proposed strategies for scalable video compression. Video sequences reconstructed at reduced frame-rates, from subsets of the compressed bit-stream, demonstrate the visually pleasing properties expected from low-pass filtering along the motion trajectories. The paper also describes a compact representation for the motion parameters, having motion overhead comparable to that of motion-compensated predictive coders. Our experimental results compare favourably with others reported in the literature, however, the principle objective of this paper is to motivate a new framework for highly scalable video compression.

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video--telephony or multimedia applications some loss of information is usually tolerated in exchange for a high compression ratio.

In this paper, an embedded wavelet based image coding algorithm is described. The algorithm allows certain regions of the image to be coded losslessly so that they can be exactly recovered by the decoder, while the remaining part is coded in a lossy manner. This maintains high compression while meeting the requirement of having lossless Regions of Interest (ROI’s) in certain applications, like medical imaging. All coding, regional and full image, is done in a naturally progressive way all the way up to lossless. 1.

ABSTRACT: Lossless image compression with progressive transmission capabilities plays a key role in measurement applications, requiring quantitative analysis and involving large sets of images. This work proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new technique for the coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of multimodal medical images show that the proposed algorithm outperforms the embedded zerotree coder combined with the integer wavelet transform by 0.28 bpp, the set-partitioning coder by 0.1 bpp, and the lossless JPEG coder by 0.6 bpp. The scheme produces a losslessly compressed embedded data stream; hence, it supports progressive refinement of the decompressed images. Therefore, it is a good candidate for telematics applications requiring fast user interaction with the image data, retaining the option of lossless transmission and archiving of the

This paper describes a method which use the well known S+P and TT transforms to encode an especially important part an image, a Region Of Interest (ROI) in a lossless mode. Other parts of the image (the Background) are given lower quality levels allowing higher compression. The ROI coding is done in the framework of an embedded wavelet based image compression algorithm. All coding, regional and full image, is done in a naturally progressive manner all the way up to lossless. 1.

Many classes of images contain some spatial regions which are more important than other regions. Compression methods which are capable of delivering higher reconstruction quality for the important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Algorithms which deliver lossless compression within the regions of interest, and lossy compression elsewhere in the image, might be the key to providing efficient and accurate image coding to the medical community. We present and compare several new algorithms for lossless region-of-interest (ROI) compression. One is based on lossless coding with the S-transform, and two are based on lossy wavelet zerotree coding together with either pixel-domain or transform-domain coding of the regional residual. We survey previous methods for region-based coding of medical images.