We present the unequal loss protection (ULP) framework in which unequal amounts of forward error correction are applied to progressive data to provide graceful degradation of image quality as packet losses increase. We develop a simple algorithm that can find a good assignment within the ULP framework. We use the Set Partitioning in Hierarchical Trees coder in this work, but our algorithm can protect any progressive compression scheme. In addition, we promote the use of a PMF of expected channel conditions so that our system can work with almost any model or estimate of packet losses. We find that when optimizing for an exponential packet loss model with a mean loss rate of 20 % and using a total rate of 0.2 bits per pixel on the Lenna image, good image quality can be obtained even when 40% of transmitted packets are lost.

Our thesis is that high compression efficiency for text and images can be obtained by using sophisticated statistical compression techniques, and that greatly increased speed can be achieved at only a small cost in compression efficiency. Our emphasis is on elegant design and mathematical as well as empirical analysis. We analyze arithmetic coding as it is commonly implemented and show rigorously that almost no compression is lost in the implementation. We show that high-efficiency lossless compression of both text and grayscale images can be obtained by using appropriate models in conjunction with arithmetic coding. We introduce a four-component paradigm for lossless image compression and present two methods that give state of the art compression efficiency. In the text compression area, we give a small improvement on the preferred method in the literature. We show that we can often obtain significantly improved throughput at the cost of slightly reduced compression. The extra speed c...

We present an algorithm that assigns unequal amounts of forward error correction to progressive data so as to provide graceful degradation as packet losses increase. We use the SPIHT coder to compress images in this work, but our algorithm can protect any progressive compression scheme. The algorithm can also use almost any function as a model of packet loss conditions. We find that for an exponential packet loss model with a mean of 20% and a total rate of 0.2 bpp, good image quality can be obtained, even when 40% of transmitted packets are lost. 1 Introduction The Internet is a widely deployed network of computers that allows the exchange of data packets. In traversing the network, a packet is sent from computer to computer until it arrives at its destination. Because of congestion, however, packets are often discarded, causing loss of data and most likely decoding failure if the lost data are not replaced. When each packet is assigned a unique sequence number, it is known which pac...

Several techniques based on the 3D Discrete Cosine Transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3D wavelet coders that do meet these requirements, and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles and context-based arithmetic coding. Additionally, a new 3D DCT-based coding scheme is designed, and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level, and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared to the state-of-the-art. Index Terms Volumetric coding, medical image compression, lossless compression, embedded coding, quadtree coding, layered zero coding, progressive image transmission, JPEG2000.

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This paper presents a new realization of the Inverse Discrete Cosine Transform (IDCT). It exploits both the decorrelation properties of the Discrete Cosine Transform (DCT) and the quantization process that is frequently applied to the DCT's resultant coefficients. This formulation has several advantages over previous approaches, including the elimination of multiplies from the central loop of the algorithm and its adaptability to incremental evaluation. The technique provides a significant reduction in computational requirements of the IDCT, enabling a software-based implementation to perform at rates which were previously achievable only through dedicated hardware.

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

We study codeword index assignment to allow for progressive image transmission of fixed rate full search vector quantization (VQ). We develop three new methods of assigning indices to a vector quantization codebook and formulate these assignments as labels of nodes of a full search progressive transmission tree. The tree is used to design intermediate codewords for the decoder so that full search VQ has a successive approximation character. The binary representation for the path through the tree represents the progressive transmission code. The methods of designing the tree which we apply are the generalized Lloyd algorithm, minimum cost perfect matching from optimization theory, and a method of principal component partitioning. Our empirical results show that the final method gives intermediate signal-to-noise ratios (SNR) that are close to those obtained with tree-structured vector quantization yet have higher final SNRs. I Introduction Data compression techniques have become importa...

An important issue in scientific visualization systems is the management of data sets. Most data sets in scientific visualization, whether created by measurement or simulation, are usually voluminous. The goal of data management is to reduce the storage space and the access time of these data sets to speed up the visualization process. A new progressive transmission scheme using spline biorthogonal wavelet bases is proposed in this paper. By exploiting the properties of this set of wavelet bases, a fast algorithm involving only additions and subtractions is developed. Due to the multiresolutional nature of the wavelet transform, this scheme is compatible with hierarchical--structured rendering algorithms. The formula for reconstructing the functional values in a continuous volume space is given in a simple polynomial form. Lossless compression is possible, even when using floating--point numbers. This algorithm has been applied to data from a global ocean model. The lossless compressio...

In this paper we present a new algorithm that utilizes mathematical morphology for pyramidal coding of color images. We obtain lossy color image compression by using Block Truncation Coding at the pyramid levels to attain reduced data rates. The pyramid approach is attractive due to low computational complexity, simple parallel implementation, and the ability to produce acceptable color images at moderate data rates. In many applications the progressive transmission capability of the algorithm is very useful. We will show experimental results for color images at data rates of 1.89 bits/pixel. This work was partially supported by the National Science Foundation, under Grant No. CDR-8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, and the National Science Foundation Graduate Fellowship. 1. INTRODUCTION The pyramid structure for image representation provides a means for processing images at multiple resolutions [1,2]. The pyramid representation of an ima...