Showing papers on "Data compression published in 2000"
TL;DR: An adaptive, data-driven threshold for image denoising via wavelet soft-thresholding derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution widely used in image processing applications.
Abstract: The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.
2,917 citations
TL;DR: A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT), capable of modeling the spatially varying visual masking phenomenon.
Abstract: A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich set of features, including resolution and SNR scalability together with a "random access" property. The algorithm has modest complexity and is suitable for applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.
1,933 citations
TL;DR: LOCO-I as discussed by the authors is a low complexity projection of the universal context modeling paradigm, matching its modeling unit to a simple coding unit, which is based on a simple fixed context model, which approaches the capability of more complex universal techniques for capturing high-order dependencies.
Abstract: LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.
1,668 citations
TL;DR: It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.
Abstract: With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed, the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and functionalities that current standards can either not address efficiently or in many cases cannot address at all. Lossless and lossy compression, embedded lossy to lossless coding, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.
1,485 citations
TL;DR: This work proposes an algorithm to optimally estimate the overall distortion of decoder frame reconstruction due to quantization, error propagation, and error concealment and recursively computes the total decoder distortion at pixel level precision to accurately account for spatial and temporal error propagation.
Abstract: Resilience to packet loss is a critical requirement in predictive video coding for transmission over packet-switched networks, since the prediction loop propagates errors and causes substantial degradation in video quality. This work proposes an algorithm to optimally estimate the overall distortion of decoder frame reconstruction due to quantization, error propagation, and error concealment. The method recursively computes the total decoder distortion at pixel level precision to accurately account for spatial and temporal error propagation. The accuracy of the estimate is demonstrated via simulation results. The estimate is integrated into a rate-distortion (RD)-based framework for optimal switching between intra-coding and inter-coding modes per macroblock. The cost in computational complexity is modest. The framework is further extended to optimally exploit feedback/acknowledgment information from the receiver/network. Simulation results both with and without a feedback channel demonstrate that precise distortion estimation enables the coder to achieve substantial and consistent gains in PSNR over known state-of-the-art RD- and non-RD-based mode switching methods.
717 citations
01 Jul 2000
TL;DR: A new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning, coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction sees improvements in error by a factor four compared to other progressive coding schemes.
Abstract: We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes.
630 citations
01 Jul 2000
TL;DR: In this paper, spectral methods are applied to 3D mesh data to obtain compact representations, which is achieved by projecting the mesh geometry onto an orthonormal basis derived from the mesh topology.
Abstract: We show how spectral methods may be applied to 3D mesh data to obtain compact representations. This is achieved by projecting the mesh geometry onto an orthonormal basis derived from the mesh topology. To reduce complexity, the mesh is partitioned into a number of balanced submeshes with minimal interaction, each of which are compressed independently. Our methods may be used for compression and progressive transmission of 3D content, and are shown to be vastly superior to existing methods using spatial techniques, if slight loss can be tolerated.
607 citations
TL;DR: A new algorithm based on polar maps is detailed for the accurate and efficient recovery of the template in an image which has undergone a general affine transformation and results are presented which demonstrate the robustness of the method against some common image processing operations.
Abstract: Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyrighted material. This paper describes a method for the secure and robust copyright protection of digital images. We present an approach for embedding a digital watermark into an image using the Fourier transform. To this watermark is added a template in the Fourier transform domain to render the method robust against general linear transformations. We detail a new algorithm based on polar maps for the accurate and efficient recovery of the template in an image which has undergone a general affine transformation. We also present results which demonstrate the robustness of the method against some common image processing operations such as compression, rotation, scaling, and aspect ratio changes.
585 citations
TL;DR: The majority of the article is devoted to the techniques developed for block-based hybrid coders using motion-compensated prediction and transform coding, and a separate section covers error resilience techniques for shape coding in MPEG-4.
Abstract: We review error resilience techniques for real-time video transport over unreliable networks. Topics covered include an introduction to today's protocol and network environments and their characteristics, encoder error resilience tools, decoder error concealment techniques, as well as techniques that require cooperation between encoder, decoder, and the network. We provide a review of general principles of these techniques as well as specific implementations adopted by the H.263 and MPEG-4 video coding standards. The majority of the article is devoted to the techniques developed for block-based hybrid coders using motion-compensated prediction and transform coding. A separate section covers error resilience techniques for shape coding in MPEG-4.
578 citations
TL;DR: A low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding, which allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream.
Abstract: We propose a low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding. Three-dimensional spatio-temporal orientation trees coupled with powerful SPIHT sorting and refinement renders 3-D SPIHT video coder so efficient that it provides comparable performance to H.263 objectively and subjectively when operated at the bit rates of 30 to 60 kbits/s with minimal system complexity. Extension to color-embedded video coding is accomplished without explicit bit allocation, and can be used for any color plane representation. In addition to being rate scalable, the proposed video coder allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream. This added functionality along with many desirable attributes, such as full embeddedness for progressive transmission, precise rate control for constant bit-rate traffic, and low complexity for possible software-only video applications, makes the proposed video coder an attractive candidate for multimedia applications.
560 citations
TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
Abstract: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.
TL;DR: The results of a performance evaluation and characterization of a number of shot-change detection methods that use color histograms, block motion matching, or MPEG compressed data are presented.
Abstract: A number of automated shot-change detection methods for indexing a video sequence to facilitate browsing and retrieval have been proposed. Many of these methods use color histograms or features computed from block motion or compression parameters to compute frame differences. It is important to evaluate and characterize their performance so as to deliver a single set of algorithms that may be used by other researchers for indexing video databases. We present the results of a performance evaluation and characterization of a number of shot-change detection methods that use color histograms, block motion matching, or MPEG compressed data.
TL;DR: A sliding-window method for data selection is used to mitigate the impact of a scene change and the data points for updating a model are adaptively selected such that the statistical behavior is improved.
Abstract: This paper presents a scalable rate control (SRC) scheme based on a more accurate second-order rate-distortion model. A sliding-window method for data selection is used to mitigate the impact of a scene change. The data points for updating a model are adaptively selected such that the statistical behavior is improved. For video object (VO) shape coding, we use an adaptive threshold method to remove shape-coding artifacts for MPEG-4 applications. A dynamic bit allocation among VOs is implemented according to the coding complexities for each VO. SRC achieves more accurate bit allocation with low latency and limited buffer size. In a single framework, SRC offers multiple layers of controls for objects, frames, and macroblocks (MBs). At MB level, SRC provides finer bit rate and buffer control. At multiple VO level, SRC offers superior VO presentation for multimedia applications. The proposed SRC scheme has been adopted as part of the International Standard of the emerging ISO MPEG-4 standard.
TL;DR: At low bit rates, reversible integer-to-integer and conventional versions of transforms were found to often yield results of comparable quality, with the best choice for a given application depending on the relative importance of the preceding criteria.
Abstract: In the context of image coding, a number of reversible integer-to-integer wavelet transforms are compared on the basis of their lossy compression performance, lossless compression performance, and computational complexity. Of the transforms considered, several were found to perform particularly well, with the best choice for a given application depending on the relative importance of the preceding criteria. Reversible integer-to-integer versions of numerous transforms are also compared to their conventional (i.e., nonreversible real-to-real) counterparts for lossy compression. At low bit rates, reversible integer-to-integer and conventional versions of transforms were found to often yield results of comparable quality. Factors affecting the compression performance of reversible integer-to-integer wavelet transforms are also presented, supported by both experimental data and theoretical arguments.
TL;DR: A line-based approach for the implementation of the wavelet transform is introduced, which yields the same results as a "normal" implementation, but where, unlike prior work, the memory issues arising from the need to synchronize encoder and decoder are addressed.
Abstract: This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether both the wavelet transforms and the subsequent coding can be implemented in low memory without significant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. There are two main contributions of our work. First, we introduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a "normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coefficients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.
TL;DR: This work transcoding of pre-encoded MPEG-1, 2 video into lower bit rates is realized through altering the coding algorithm into H.261/H.263 standards with lower spatio-temporal resolutions through heterogeneous transcoding.
Abstract: In this work, transcoding of pre-encoded MPEG-1, 2 video into lower bit rates is realized through altering the coding algorithm into H.261/H.263 standards with lower spatio-temporal resolutions. For this heterogeneous transcoding, we extract and compose a set of candidate motion vectors, from the incoming bit stream, to comply with the encoding format of the output bit stream. For the spatial resolution reduction we generate one motion vector out of a set of input motion vectors operating on the higher spatial resolution image. Finally, for the temporal resolution reduction we compose new motion vectors from the dropped frames motion vectors. Throughout the paper, we discuss the impact of motion estimation refinement on the new motion vectors and show that for all cases a simple half-pixel refinement is sufficient for near-optimum results.
01 Oct 2000
TL;DR: The authors describe various discrete transforms and their applications in different disciplines and demonstrate their power and practicality in data compression.
Abstract: From the Publisher:
The Transform and Data Compression Handbook serves as a handbook for a wide range of researchers and engineers." "The authors describe various discrete transforms and their applications in different disciplines. They cover techniques, such as adaptive quantization and entropy coding, that result in significant reduction in bit rates when applied to the transform coefficients. With presentations of the ideas and concepts, as well as descriptions of the algorithms, the authors provide insight into the applications and their limitations. Data compression is an essential step towards the efficient storage and transmission of information. The Transform and Data Compression Handbook provides information regarding different discrete transforms and demonstrates their power and practicality in data compression.
01 Feb 2000
TL;DR: This work develops a compressed index, called the IQ-tree, with a three-level structure, and develops a cost model and an optimization algorithm based on this cost model that permits an independent determination of the degree of compression for each second level page to minimize expected query cost.
Abstract: Two major approaches have been proposed to efficiently process queries in databases: speeding up the search by using index structures, and speeding up the search by operating on a compressed database, such as a signature file. Both approaches have their limitations: indexing techniques are inefficient in extreme configurations, such as high-dimensional spaces, where even a simple scan may be cheaper than an index-based search. Compression techniques are not very efficient in all other situations. We propose to combine both techniques to search for nearest neighbors in a high-dimensional space. For this purpose, we develop a compressed index, called the IQ-tree, with a three-level structure: the first level is a regular (flat) directory consisting of minimum bounding boxes, the second level contains data points in a compressed representation, and the third level contains the actual data. We overcome several engineering challenges in constructing an effective index structure of this type. The most significant of these is to decide how much to compress at the second level. Too much compression will lead to many needless expensive accesses to the third level. Too little compression will increase both the storage and the access cost for the first two levels. We develop a cost model and an optimization algorithm based on this cost model that permits an independent determination of the degree of compression for each second level page to minimize expected query cost. In an experimental evaluation, we demonstrate that the IQ-tree shows a performance that is the "best of both worlds" for a wide range of data distributions and dimensionalities.
TL;DR: The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies.
Abstract: The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms.
TL;DR: Two new coding schemes are introduced, probabilistic reasoning models (PRM) and enhanced FLD (Fisher linear discriminant) models (EFM), for indexing and retrieval of large image databases with applications to face recognition.
Abstract: This paper introduces two new coding schemes, probabilistic reasoning models (PRM) and enhanced FLD (Fisher linear discriminant) models (EFM), for indexing and retrieval of large image databases with applications to face recognition. The unifying theme of the new schemes is that of lowering the space dimension ("data compression") subject to increased fitness for the discrimination index.
TL;DR: This work considers the problem of coding images for transmission over error-prone channels and proposes algorithms that attain similar PSNR values using typically about 50-60% of the bit rate required by these other state-of-the-art coders, while providing significantly more freedom in the mechanism for allocation of redundancy among descriptions.
Abstract: We consider the problem of coding images for transmission over error-prone channels. The impairments we target are transient channel shutdowns, as would occur in a packet network when a packet is lost, or in a wireless system during a deep fade: when data is delivered it is assumed to be error-free, but some of the data may never reach the receiver. The proposed algorithms are based on a combination of multiple description scalar quantizers with techniques successfully applied to the construction of some of the most efficient subband coders. A given image is encoded into multiple independent packets of roughly equal length. When packets are lost, the quality of the approximation computed at the receiver depends only on the number of packets received, but does not depend on exactly which packets are actually received. When compared with previously reported results on the performance of robust image coders based on multiple descriptions, on standard test images, our coders attain similar PSNR values using typically about 50-60% of the bit rate required by these other state-of-the-art coders, while at the same time providing significantly more freedom in the mechanism for allocation of redundancy among descriptions.
TL;DR: Li et al. as mentioned in this paper proposed a semi-fragile watermarking technique that accepts JPEG lossy compression on the watermarked image to a pre-determined quality factor, and rejects malicious attacks.
Abstract: In this paper, we propose a semi-fragile watermarking technique that accepts JPEG lossy compression on the watermarked image to a pre-determined quality factor, and rejects malicious attacks. The authenticator can identify the positions of corrupted blocks, and recover them with approximation of the original ones. In addition to JPEG compression, adjustments of the brightness of the image within reasonable ranges, are also acceptable using the proposed authenticator. The security of the proposed method is achieved by using the secret block mapping function which controls the signature generating/embedding processes. Our authenticator is based on two invariant properties of DCT coefficients before and after JPEG compressions. They are deterministic so that no probabilistic decision is needed in the system. The first property shows that if we modify a DCT coefficient to an integral multiple of a quantization step, which is larger than the steps used in later JPEG compressions, then this coefficient can be exactly reconstructed after later acceptable JPEG compression. The second one is the invariant relationships between two coefficients in a block pair before and after JPEG compression. Therefore, we can use the second property to generate authentication signature, and use the first property to embed it as watermarks. There is no perceptible degradation between the watermarked image and the original. In additional to authentication signatures, we can also embed the recovery bits for recovering approximate pixel values in corrupted areas. Our authenticator utilizes the compressed bitstream, and thus avoids rounding errors in reconstructing DCT coefficients. Experimental results showed the effectiveness of this system. The system also guaranies no false alarms, i.e., no acceptable JPEG compression is rejected.
TL;DR: It is shown that in certain structured settings, it is possible to obtain reliable estimates of camera motion by directly processing data easily obtained from the MPEG format.
Abstract: As digital video becomes more pervasive, efficient ways of searching and annotating video according to content will be increasingly important. Such tasks arise, for example, in the management of digital video libraries for content-based retrieval and browsing. We develop tools based on camera motion for analyzing and annotating a class of structured video using the low-level information available directly from MPEG-compressed video. In particular, we show that in certain structured settings, it is possible to obtain reliable estimates of camera motion by directly processing data easily obtained from the MPEG format. Working directly with the compressed video greatly reduces the processing time and enhances storage efficiency. As an illustration of this idea, we have developed a simple basketball annotation system which combines the low-level information extracted from an MPEG stream with the prior knowledge of basketball structure to provide high-level content analysis, annotation, and browsing for events such as wide-angle and close-up views, fast breaks, probable shots at the basket, etc. The methods used in this example should also be useful in the analysis of high-level content of structured video in other domains.
08 Apr 2000
TL;DR: A theory of measuring the relatedness between two DNA sequences, and strong experimental support for this theory is presented, which is demonstrated by correctly constructing a 16S (18S) rRNA tree, and a whole genome tree for several species of bacteria.
Abstract: We present a lossless compression algorithm, Gen-Compress, for DNA sequences, based on searching for approximate repeats. Our algorithm achieves the best compression ratios for benchmark DNA sequences, comparing to other DNA compression programs [3, 7]. Significantly better compression results show that the approximate repeats are one of the main hidden regularities in DNA sequences.We then describe a theory of measuring the relatedness between two DNA sequences. We propose to use d(x, y) = 1 — K(x) - K(x|y)/K(xy to measure the distance of any two sequences, where K stands for Kolmogorov complexity [5]. Here, K(x) - K(x|y) is the mutual information shared by x and y. But mutual information is not a distance, there is no triangle inequality. The distance d(x, y) is symmetric. It also satisfies the triangle inequality, and furthermore, it is universal [4].It has not escaped our notice that the distance measure we have postulated can be immediately used to construct evolutionary trees from DNA sequences, especially those that cannot be aligned, such as complete genomes. With more and more genomes sequenced, constructing trees from genomes becomes possible [1, 2, 6, 8]. Kolmogorov complexity is not computable. We use GenCompress to approximate it. We present strong experimental support for this theory, and demonstrate its applicability by correctly constructing a 16S (18S) rRNA tree, and a whole genome tree for several species of bacteria. Larger scale experiments are underway at the University of Waterloo, with very promising results.
01 Nov 2000
TL;DR: A compression scheme is developed that is a combination of a simple but powerful phrase derivation method and a compact dictionary encoding that is highly efficient, particularly in decompression, and has characteristics that make it a favorable choice when compressed data is to be searched directly.
Abstract: Dictionary-based modeling is a mechanism used in many practical compression schemes. In most implementations of dictionary-based compression the encoder operates on-line, incrementally inferring its dictionary of available phrases from previous parts of the message. An alternative approach is to use the full message to infer a complete dictionary in advance, and include an explicit representation of the dictionary as part of the compressed message. In this investigation, we develop a compression scheme that is a combination of a simple but powerful phrase derivation method and a compact dictionary encoding. The scheme is highly efficient, particularly in decompression, and has characteristics that make it a favorable choice when compressed data is to be searched directly. We describe data structures and algorithms that allow our mechanism to operate in linear time and space.
TL;DR: An effective method for increasing error resilience of video transmission over bit error prone networks is described and rate-distortion optimized mode selection and synchronization marker insertion algorithms are introduced.
Abstract: We describe an effective method for increasing error resilience of video transmission over bit error prone networks. Rate-distortion optimized mode selection and synchronization marker insertion algorithms are introduced. The resulting video communication system takes into account the channel condition and the error concealment method used by the decoder, to optimize video coding mode selection and placement of synchronization markers in the compressed bit stream. The effects of mismatch between the parameters used by the encoder and the parameters associated with the actual channel condition and the decoder error concealment method are evaluated. Results for the binary symmetric channel and wideband code division multiple access mobile network models are presented in order to illustrate the advantages of the proposed method.
TL;DR: This work develops a framework for encoding based on embedded source codes and embedded error correcting and error detecting channel codes and shows that the unequal error/erasure protection policies that maximize the average useful source coding rate allow progressive transmission with optimal unequal protection at a number of intermediate rates.
Abstract: An embedded source code allows the decoder to reconstruct the source progressively from the prefixes of a single bit stream. It is desirable to design joint source-channel coding schemes which retain the capability of progressive reconstruction in the presence of channel noise or packet loss. Here, we address the problem of joint source-channel coding of images for progressive transmission over memoryless bit error or packet erasure channels. We develop a framework for encoding based on embedded source codes and embedded error correcting and error detecting channel codes. For a target transmission rate, we provide solutions and an algorithm for the design of optimal unequal error/erasure protection. Three performance measures are considered: the average distortion, the average peak signal-to-noise ratio, and the average useful source coding rate. Under the assumption of rate compatibility of the underlying channel codes, we provide necessary conditions for progressive transmission of joint source-channel codes. We also show that the unequal error/erasure protection policies that maximize the average useful source coding rate allow progressive transmission with optimal unequal protection at a number of intermediate rates.
TL;DR: A method for radical linear compression of data sets where the data are dependent on some number M of parameters is presented, and it is shown that, if the noise in the data is independent of the parameters, it can form M linear combinations of the data which contain as much information about all the parameters as the entire data set.
Abstract: We present a method for radical linear compression of data sets where the data are dependent on some number M of parameters. We show that, if the noise in the data is independent of the parameters, we can form M linear combinations of the data which contain as much information about all the parameters as the entire data set, in the sense that the Fisher information matrices are identical; i.e. the method is lossless. We explore how these compressed numbers fare when the noise is dependent on the parameters, and show that the method, though not precisely lossless, increases errors by a very modest factor. The method is general, but we illustrate it with a problem for which it is well-suited: galaxy spectra, the data for which typically consist of similar to 10(3) fluxes, and the properties of which are set by a handful of parameters such as age, and a parametrized star formation history. The spectra are reduced to a small number of data, which are connected to the physical processes entering the problem. This data compression offers the possibility of a large increase in the speed of determining physical parameters. This is an important consideration as data sets of galaxy spectra reach 10(6) in size, and the complexity of model spectra increases. In addition to this practical advantage, the compressed data may offer a classification scheme for galaxy spectra which is based rather directly on physical processes.
28 May 2000
TL;DR: The experimental results show that the proposed algorithm outperforms the respected zero-tree/-block coders, SPIHT and SPECK, in compression efficiency and is comparable to the state-of-art JPEG 2000 test coder in PSNR performance while retaining the attractive low-complexity feature of the zeroblock coders.
Abstract: With fast computation and excellent compression efficiency, two embedded coding techniques, zero-tree/-block coding and context modeling of the subband/wavelet coefficients, have been widely utilized for image coding applications. In this research, we present a new embedded wavelet image coding algorithm with an attempt to combine advantages of these two successful coding schemes. The experimental results show that the proposed algorithm outperforms the respected zero-tree/-block coders, SPIHT and SPECK, in compression efficiency. It is also comparable to the state-of-art JPEG 2000 test coder in PSNR performance while retaining the attractive low-complexity feature of the zeroblock coders.