Block Coders Research Articles

CT and MRI image compression using wavelet-based contourlet transform and binary array technique

Compression techniques are essential for efficient storage and fast transfer of medical image data. In this paper, a rapid 2-D lossy compression technique constructed using wavelet-based contourlet transform (WBCT) and binary array technique (BAT) has been proposed for computed tomography (CT) and magnetic resonance imaging (MRI) images. In WBCT, the high-frequency subband obtained from wavelet transform is further decomposed into multiple directional subbands by directional filter bank to obtain more directional information. The relationship between the coefficients has been changed in WBCT as it has more directions. The differences in parent---child relationships are handled by a repositioning algorithm. The repositioned coefficients are then subjected to quantization. The quantized coefficients are further compressed by BAT where the most frequently occurring value is coded only once. The proposed method has been experimented with real-time CT and MRI images, the results indicated that the processing time of the proposed method is less compared to existing wavelet-based set-partitioning in hierarchical trees and set-partitioning embedded block coders. The evaluation results obtained from radiologists indicated that the proposed method could reproduce the diagnostic features of CT and MRI images precisely.

Design and Analysis of System on a Chip Encoder for JPEG2000

Much work has been performed on optimizing the throughput of the block coding system within JPEG2000. However, the question remains as to whether providing parallel simple block coders provides a cheaper method of increasing throughput than complicated optimized block coders. We present the analysis and results for a system on a chip (SoC) software/hardware codesign platform, for parallel coding in JPEG2000 compression standard. We design both a simple and a high performance, optimized peripheral encoder as a hardware accelerator for the JPEG2000 SoC encoding system. The system is implemented on an Altera NIOS II processor with flexible integrated peripheral. We show that there are optimum numbers of parallel block coders and scheduling granularity per row of codeblocks, and that parallel optimized encoders outperform parallel simple encoders. We also demonstrate that the block coding system becomes work starved rather than memory blocked when many parallel coders are present, indicating a discrete wavelet transform bottleneck.

Warped Discrete Cosine Transform-Based Low Bit-Rate Block Coding Using Image Downsampling

This paper presents warped discrete cosine transform (WDCT)-based low bit-rate block coding using image downsampling. While WDCT aims to improve the performance of conventional DCT by frequency warping, the WDCT has only been applicable to high bit-rate coding applications because of the overhead required to define the parameters of the warping filter. Recently, low bit-rate block coding based on image downsampling prior to block coding followed by upsampling after the decoding process is proposed to improve the compression performance for low bit-rate block coders. This paper demonstrates that a superior performance can be achieved if WDCT is used in conjunction with image downsampling-based block coding for low bit-rate applications.

Undersampled Boundary Pre-/Postfilters for Low Bit-Rate DCT-Based Block Coders

It has been well established that critically sampled boundary pre-/postfiltering operators can improve the coding efficiency and mitigate blocking artifacts in traditional discrete cosine transform-based block coders at low bit rates. In these systems, both the prefilter and the postfilter are square matrices. This paper proposes to use undersampled boundary pre- and postfiltering modules, where the pre-/postfilters are rectangular matrices. Specifically, the prefilter is a "fat" matrix, while the postfilter is a "tall" one. In this way, the size of the prefiltered image is smaller than that of the original input image, which leads to improved compression performance and reduced computational complexities at low bit rates. The design and VLSI-friendly implementation of the undersampled pre-/postfilters are derived. Their relations to lapped transforms and filter banks are also presented. Two design examples are also included to demonstrate the validity of the theory. Furthermore, image coding results indicate that the proposed undersampled pre-/postfiltering systems yield excellent and stable performance in low bit-rate image coding.

Variable projection for near-optimal filtering in low bit-rate block coders

Recent work on block-based compression for low bit-rate coding has shown that employing a block coder within a sampling scheme where the image is downsampled prior to coding (and upsampled after the decoding stage) results in superior performance compared to standard block coding. We explore the use of optimal decimation and interpolation filters in this coding scheme. We show that the problem of finding optimal filters for a general, unknown, "black-box" coder can be written as a separable least squares problem in two sets of variables. We then elegantly solve this optimization problem using the Variable Projection method. The experimental results presented clearly exhibit a significant improvement over existing approaches.

Variable projection for near-optimal filtering in low bit-rate block coders

Recent work on block-based compression for low bit-rate coding has shown that employing a block coder within a sampling scheme where the image is downsampled prior to coding (and upsampled after the decoding stage) results in superior performance compared to standard block coding. We explore the use of optimal decimation and interpolation filters in this coding scheme. We show that the problem of finding optimal filters for a general, unknown, black-box coder can be written as a separable least squares problem in two sets of variables. We then elegantly solve this optimization problem using the Variable Projection method. The experimental results presented clearly exhibit a significant improvement over existing approaches.

A wavelet-based analysis of fractal image compression

Why does fractal image compression work? What is the implicit image model underlying fractal block coding? How can we characterize the types of images for which fractal block coders will work well? These are the central issues we address. We introduce a new wavelet-based framework for analyzing block-based fractal compression schemes. Within this framework we are able to draw upon insights from the well-established transform coder paradigm in order to address the issue of why fractal block coders work. We show that fractal block coders of the form introduced by Jacquin (1992) are Haar wavelet subtree quantization schemes. We examine a generalization of the schemes to smooth wavelets with additional vanishing moments. The performance of our generalized coder is comparable to the best results in the literature for a Jacquin-style coding scheme. Our wavelet framework gives new insight into the convergence properties of fractal block coders, and it leads us to develop an unconditionally convergent scheme with a fast decoding algorithm. Our experiments with this new algorithm indicate that fractal coders derive much of their effectiveness from their ability to efficiently represent wavelet zero trees. Finally, our framework reveals some of the fundamental limitations of current fractal compression schemes.

Image coding by block prediction of multiresolution subimages

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.

Nonlinear space-variant postprocessing of block coded images

An important application of spatial filtering techniques is in the postprocessing of images degraded by coding. Linear, space-invariant filters are inadequate to reduce the noise produced by block coders. The noise in block coded images is correlated with the local characteristics of the signal, and such filters are unable to exploit this correlation to reduce the noise. We propose a new nonlinear, space-variant filtering algorithm which smooths jagged edges without blurring them, and smooths out abrupt intensity changes in monotone areas. Edge sharpness is preserved because near edges the filtering of the signal is negligible. Consequently, in-band noise is not reduced, but the well-known masking effect reduces the visibility of this in-band noise. The algorithm is only slightly more complex to implement than simple linear filtering. We present examples of processed images and SNR figures to demonstrate that a significant improvement in subjective and objective quality is achieved.

A method for proving multiterminal source coding theorems

The two-step method used by Wyner and Ziv to prove the Wyner-Ziv theorem is extended to prove sliding-block source coding theorems for coding a general finite-alphabet ergodic multiterminal source with respect to a single-letter fidelity criterion. The first step replaces every stochastic encoding in the network by a deterministic sliding-block encoding. The second step involves using the Slepian-Wolf theorem to adjust the sliding-block encodings so that they will have the desired rate. while introducing little additional distortion. The method is applied to give quick proofs of sliding-block versions of theorems of Berger, Kaspi, and Tung. Since the method obtains sliding-block coders directly without first obtaining block coders, the block coding results can be obtained as an easy corollary. The direct methods for proving results on block coding are more difficult and do not imply the corresponding results on sliding-block coding. Indeed, in the multiterminal case it is, in general, not known how to construct good sliding-block coders from good block coders.

Two-Dimensional Markov Representations of Sampled Images

This paper shows that, under certain weak restrictions, a two-dimensional discrete Markov process can be represented either "causally" by a one-sided difference equation, or "noncausally" by a multiple-sided difference equation. The former representation is pertinent to the analysis of image coders and processors that operate sequentially on scanned image data. The latter representation is applicable to the analysis of block coders and processors. The general relation between the two representations is given and the specialized forms resulting under separable scene covariance studied.

Linear-Real Codes and Coders*

In linear-real coding, the transmitted signals are (possibly redundant) linear combinations of the data signals. The linear combination of data signals can have a block pattern, resulting in linear-real block coders, or a stationary pattern, resulting in linear-real stationary (shift-register) coders. Stationary coding is shown to be a limiting case of block coding. Both methods appear to be practical for the control of burst and impulse noise. However, stationary coding appears to have some advantages and is the only one we study here. We propose shift register implementations which promise the required precision and dispersion at less cost than tuned RLC circuits. Error properties of both block and stationary coders are similar, but it is easier to learn concepts by analyzing the block coders. When the receiver is able, by using some of the techniques we discuss, to estimate the noise covariance matrix for each codeblock, the resulting noise power is less than that for receivers not using the statistics for each codeblock. Nonlinear memoryless filters, such as clippers, are especially effective when used with linear-real coders. We propose a memoryless filter which attenuates the input signal more severely when a second input to the filter indicates the channel is having a noise burst. If the memoryless filter is designed for the worst case noise, then performance will not degrade with decreased noise when the nonlinearity is odd and monotonic.