A contextual lightweight arithmetic coder is proposed for lossless compression of medical imagery. Context definition uses causal data from previous symbols coded, an inexpensive yet efficient approach. To further reduce the computational cost, a binary arithmetic coder with fixed-length codewords is adopted, thus avoiding the normalization procedure common in most implementations, and the probability of each context is estimated through bitwise operations. Experimental results are provided for several medical images and compared against state-of-the-art coding techniques, yielding on average improvements between nearly 0.1 and 0.2 bps.
Recently, Han et. al. developed a method for visually lossless compression using JPEG2000. In this method, visibility thresholds (VTs) are experimentally measured and used during quantization to ensure that the errors introduced by quantization are below these thresholds. In this work, we extend the work of Han et. al. to visually lossy regime. We propose a framework where a series of experiments are conducted to measure Just-Noticeable-Differences using the quantization distortion model introduced by Han et. al. The resulting thresholds are incorporated into a JPEG2000 encoder to yield visually lossy, JPEG2000 Part 1 compliant codestreams.
In this paper, Compressive Sensing (CS) methods for Direct Sequence Spread Spectrum (DSSS) signals are
introduced. DSSS signals are formed by modulating the original signal by a Pseudo-Noise sequence. This
modulation spreads the spectra over a large bandwidth and makes interception of DSSS signals challenging.
Interception of DSSS signals using traditional methods require Analog-to-Digital Converters sampling at very
high rates to capture the full bandwidth. In this work, we propose CS methods that can intercept DSSS
signals from compressive measurements. The proposed methods are evaluated with DSSS signals generated
using Maximum-length Sequences and Binary Phase-Shift-Keying modulation at varying signal-to-noise and
In this paper, compressive detection strategies for FHSS signals are introduced. Rapid switching of the carrier
frequency among many channels using a pseudorandom sequence makes detection of FHSS signals challenging.
The conventional approach to detect these signals is to rapidly scan small segments of the spectrum sequentially.
However, such a scanner has the inherent risk of never overlapping with the transmitted signal depending on
factors such as rate of hopping and scanning. In this paper, we propose compressive detection strategies that
sample the full spectrum in a compressive manner. Theory and simulations are presented to illustrate the benefits
of the proposed framework.
Remote visualization of volumetric data has gained importance over the past few years in order to realize the full potential of tele-radiology. Volume rendering is a computationally intensive process, often requiring hardware acceleration to achieve real time visualization. Hence a remote visualization model that is well-suited for high speed networks would be to transmit rendered images from the server (with dedicated hardware) based on view point requests from clients. In this regard, a compression scheme for the rendered images is vital for efficient utilization of the server-client bandwidth. Also, the complexity of the decompressor should be considered so that
a low end client workstation can decode images at the desired frame rate. We present a scalable low complexity image coder that has good compression efficiency and high throughput.
A novel rate-optimal rate allocation algorithm is proposed for parallel transmission of scalable images in multi-channel
systems. Scalable images are transmitted via fixed-length packets. The proposed algorithm selects a
subchannel as well as a channel code rate for each packet, based on the signal-to-noise ratios (SNR) of the
subchannels. The resulting scheme provides unequal error protection of source bits. Applications to JPEG2000
transmission show that significant UEP gains are achieved over equal error protection (EEP) schemes.
There is growing interest in computer aided diagnosis applications including automatic detection of lung nodules from multislice computed tomography (CT). However the increase in the number and size of CT datasets introduces high costs for data storage and transmission, and becomes an obstacle to routine clinical exam as well as hindering widespread utilization of computerized applications. We investigated the effects of 3D lossy region-based JPEG2000 standard compression on the results of an automatic lung nodule detection system. As the algorithm detects the lungs within the datasets, we used this lung segmentation to define a region of interest (ROI) where the compression should be of higher fidelity. We tested 4 methods of 3D compression: 1) default compression of the whole image, 2) default compression of segmented lungs with masking out all non-lung regions, 3) ROI-based compression as specified in the JPEG2000 standard and 4) compression where voxels in the ROI are weighted to be given emphasis in the encoding. We tested 7 compression ratios per method: 1, 4, 6, 8, 10, 20, and 30 to 1. We then evaluated our experimental CAD algorithm on 10 patients with 67 documented nodules initially identified on the decompressed data. Sensitivities and false positive rates were compared for the various compression methods and ratios. We found that region-based compression generally performs better than default compression. The sensitivity with default compression decreased from 85% at no compression to 61% at 30:1 compression, a decrease of 25%, whereas the masked compression method saw a decreased in sensitivity on only 13.5% at maximum compression. At compression levels up to 10:1, all 3 region-based compression methods had decreases in sensitivity of 7.5% or less. Detection of small nodules (< 4mm in diameter) was more affected by compression than detection of large nodules; sensitivity to calcified nodules was less affected by compression than to non-calcified nodules.
Many medical imaging techniques available today generate 4D data sets. One such technique is functional magnetic resonance imaging (fMRI) which aims to determine regions of the brain that are activated due to various cognitive and/or motor functions or sensory stimuli. These data sets often require substantial resources for storage and transmission and hence call for efficient compression algorithms. fMRI data can be seen as a time-series of 3D images of the brain. Many different strategies can be employed for compressing such data. One possibility is to treat each 2D slice independently. Alternatively, it is also possible to compress each 3D image independently. Such methods do not fully exploit the redundancy present in 4D data. In this work, methods using 4D wavelet transforms are proposed. They are compared to different 2D and 3D methods. The proposed schemes are based on JPEG2000, which is included in the DICOM standard as a transfer syntax. Methodologies to test the effects of lossy compression on the end result of fMRI analysis are introduced and used to compare different compression algorithms.
The widespread use of multi-detector CT scanners has been associated with a remarkable increase in the number of CT slices as well as a substantial decrease in the average thickness of individual slices. This increased number of thinner slices has created a marked increase in archival and network bandwidth requirements associated with storage and transmission of these studies. We demonstrate that although compression can be used to decrease the size of these image files, thinner CT slices are less compressible than thicker slices when measured by either a visual discrimination model (VDM) or the more traditional peak signal to noise ratio. The former technique (VDM) suggests that the discrepancy in compressibility between thin and thick slices becomes greater at greater compression levels while the latter technique (PSNR), suggests that this is not the case. Previous studies that we and others have performed suggest that the VDM model probably corresponds more closely with human observers than does the PSNR model. Additionally we demonstrated that the poor relative compressibility of thin sections can be substantially negated by the use of JPEG 2000 3D compression which yields superior image quality at a given level of compression in comparison with 2D compression. Additionally, thin and thick sections are approximately equally compressible for 3D compression with little change with increasing levels of compression.
One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. Volume data is usually massive and is compressed so as to effectively utilize available network bandwidth. In our scenario, these compressed datasets are stored on a central data server and are transferred progressively to one or more clients over a network. In this paper, we study schemes that enable progressive delivery for visualization of medical volume data using JPEG2000. We then present a scheme for progressive encoding based on scene content, that enables a progression based on tissues or regions of interest in 3D medical imagery. The resulting compressed file is organized such that the tissues of interest appear in earlier segments of the bitstream. Hence a compliant decoder that chooses to stop transmission of data at a given instant would be able to render the tissue of interest with a better visual quality.
Hyperspectral images are acquired incrementally in a “push-broom” fashion by on-board sensors. Since these images are highly voluminous, buffering an entire image before compression requires a large buffer and causes latency. Incremental compression schemes work on small chunks of raw data as soon as they are acquired and help reduce buffer memory requirements. However, incremental processing leads to large variations in quality across the reconstructed image. The solution to this problem lies in using carefully designed rate control algorithms. We propose two such “leaky bucket” rate control algorithms that can be employed when incrementally compressing hyperspectral images using the JPEG2000 compression engine. They are the Multi-Layer Sliding Window Rate Controller (M-SWRC) and the Multi-Layer Extended Sliding Window Rate Controller (M-EWRC). Both schemes perform rate control using the fine granularity afforded by JPEG2000 bitstreams. The proposed algorithms have low memory requirements since they buffer compressed bitstreams rather than raw image data. Our schemes enable SNR scalability through the use of quality layers in the codestream and produce JPEG2000 compliant multi-layer codestreams at a fraction of the memory used by conventional schemes. Experiments show that the proposed schemes provide significant reduction in quality variation with no loss in mean overall PSNR performance.
We present a framework for optimal rate allocation to image subbands to minimize the distortion in the joint compression and classification of JPEG2000-compressed images. The distortion due to compression is defined as a weighted linear combination of the mean-square error (MSE) and the loss in the Bhattacharyya distance (BD) between the class-conditional distributions of the classes. Lossy compression with JPEG2000 is accomplished via deadzone uniform quantization of wavelet subbands. Neglecting the effect of the deadzone, expressions are derived for the distortion in the case of two classes with generalized Gaussian distributions (GGDs), based on the high-rate analysis of Poor. In this regime, the distortion function takes the form of a weighted MSE (WMSE) function, which can be minimized using reverse water-filling. We present experimental results based on synthetic data to evaluate the efficacy of the proposed rate allocation scheme. The results indicate that by varying the weight factor balancing the MSE and the Bhattacharyya distance, we can control the trade-off between these two terms in the distortion function.
We present a scheme for compressed domain interactive rendering of large volume data sets over distributed environments. The scheme exploits the distortion scalability and multi-resolution properties offered by JPEG2000 to provide a unified framework for interactive rendering over low bandwidth networks. The interactive client is provided breadth in terms of scalability in resolution, position and progressive improvement by quality. The server exploits the spatial locality offered by the DWT and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI. Contextual background information can also be made available having quality fading away from the VOI. The scheme is ideally suited for client-server setups with low bandwidth constraints, with the server maintaining the compressed volume data, to be browsed by a client with low processing power and/or memory. Rendering can be performed at a stage when the client feels that the desired quality threshold has been attained. We investigate the effects of code-block size on compression ratio, PSNR, decoding times and data transmission to arrive at an optimal code-block size for typical VOI decoding scenarios.
In this paper, a robust image transmission scheme based on JPEG2000 is proposed for packet erasure channels. Error resilience functionalities provided by JPEG2000 are utilized to control the source coding efficiency and the robustness according to channel conditions. Furthermore, together with the proposed interleaving scheme, some erasures can be recovered. Experimental results show the effectiveness of the scheme.
We develop novel methods for compressing volumetric imagery that has been generated by single platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We evaluate the performance of various two-dimensional (2D) compression schemes on the traditional 2D range representation. We then introduce a three-dimensional (3D) representation of the range measurements and show that, for lossless compression, 3D volumes compress more efficiently than 2D images by a factor of 60%.
An unequal loss protection framework for transmission of JPEG2000 codestreams over packet erasure channels is presented. A joint source-channel coding approach is adopted to form the JPEG2000 codestream and assign the appropriate amount of protection to different sections of the codestream. Experimental results indicate that the proposed scheme yields excellent performance across a wide range of packet loss rates.
Streaming media over heterogeneous lossy networks and time-varying communication channels is an active area of research. Several video coders that operate under the varying constraints of such environments have been proposed recently. Scalability has become a very desirable feature in these video coders. In this paper, we make use of a leaky-bucket rate allocation method (DBRC) that provides constant quality video under buffer constraints, and extend it in two advantageous directions. First, we present a rate control mechanism for 3D wavelet video coding using DBRC. Second, we enhance the DBRC so that it can be utilized when multiple sequences are multiplexed over a single communications channel. The goal is to allocate the capacity of the channel between sequences to achieve constant quality across all sequences.
We introduce techniques that improve the error resilience of JPEG2000 against packet losses. The presented methods operate on JPEG2000 codestreams and consider the properties of different codestream segments. Experiments indicate that error resilience of JPEG2000 codestreams against packet losses can be improved significantly using these techniques. The performance of the proposed methods compares favorably with existing algorithms.
JPEG 2000 Part 2 (extensions) contains a number of technologies that are of potential interest in remote sensing applications. These include arbitrary wavelet transforms, techniques to limit boundary artifacts in tiles, multiple component transforms, and trellis-coded quantization (TCQ). We are investigating the addition of these features to the low-memory (scan-based) implementation of JPEG 2000 Part 1. A scan-based implementation of TCQ has been realized and tested, with a very small performance loss as compared with the full image (frame-based) version. A proposed amendment to JPEG 2000 Part 2 will effect the syntax changes required to make scan-based TCQ compatible with the standard.
Proc. SPIE. 4472, Applications of Digital Image Processing XXIV
KEYWORDS: Image compression, Detection and tracking algorithms, Video, Distortion, Computer programming, Video surveillance, JPEG2000, Video compression, Digital video recorders, Wireless communications
With the increasing importance of heterogeneous networks and time-varying communication channels, such as packet-switched networks and wireless communications, fine scalability has become a highly desirable feature in both image and video coders. A single highly scalable bitstream can provide precise rate control for constant bitrate (CBR) traffic and accurate quality control for variable bitrate (VBR) traffic. In this paper, we propose two methods that provide constant quality video under buffer constraints. These methods can be used with all scalable coders. Experimental results using the Motion JPEG2000 coder demonstrate substantial benefits.
Many space-borne remote sensing missions are based on scanning sensors that create images a few lines at a time. Moreover, spacecraft typically have limited amounts of available memory, on account of weight, size and power constraints. For these reasons, the JPEG-2000 emerging standard has a requirement for stripe processing in order to meet the needs of the remote sensing profile. This paper first briefly presents the JPEG- 2000 algorithm, highlighting details pertinent to scan-based processing. A technique for meeting the stripe processing requirement is then presented. This technique use a sliding window rate control mechanism that maintains the desired average bit rate over entire images, while retaining a minimum number of bytes in memory at any given time. Results are then presented to show performance over various sliding window sizes.
After nearly three years of international development, the still-image technology for the JPEG-2000 standard is almost fully established. This paper briefly summarizes the developmental history of this standard and discusses its evolution through the Verification Model (VM) experimental and testing software which will become the first fully compliant, fully functional implementation of the new standard. The standard is then described, highlighting the data domains at various stages during the forward compression process. These data domains provide certain flexibilities which offer many of the rich set of features available with JPEG-2000. Some of these features are then described, with algorithmic examples as well as sample output from the VM.
A baseline mode of Trellis coded quantization (TCQ) is described as used in JPEG 2000 along with the results of visual evaluations which demonstrate TCQ effectiveness over scalar quantization (SQ). Furthermore, a reduced complexity TCQ mode is developed and described in detail. Numerical and visual evaluations indicate that compression performance is nearly identical to baseline TCQ, but with greatly reduced memory footprint and increased progressive image decoding facilities.
JPEG-2000 is the new image compression standard currently under development by ISO/IEC. Part I of this standard provides a “baseline” compression technology appropriate for grayscale and color imagery. Part II of the standard will provide extensions that allow for more advanced coding options, including the compression of multiple component imagery. Several different multiple component compression techniques are currently being investigated for inclusion in the JPEG-2000 standard. In this paper we apply some of these techniques toward the compression of HYDICE data. Two decorrelation techniques, 3D wavelet and Karhunen-Loeve Transform (KLT), were used along with two quantization techniques, scalar and trellis-coded (TCQ), to encode two HYDICE scenes at five different bit rates (4.0, 2.0, 1.0, 0.5, 0.25 bits/pixel/band). The chosen decorrelation and quantization techniques span the range from the simplest to the most complex multiple component compression systems being considered for inclusion in JPEG-2000. This paper reports root-mean-square-error (RMSE) and peak signal-to-noise ratio (PSNR) metrics for the compressed data. A companion paper  that follows reports on the effects of these compression techniques on exploitation of the HYDICE scenes.
Compression of a noisy source is usually a two stage problem, involving the operations of estimation (denoising) and quantization. A survey of literature on this problem reveals that for the squared error distortion measure, the best possible compression strategy is to subject the noisy source to an optimal estimator followed by an optimal quantizer for the estimate. What we present in this paper is a simple but sub-optimal vector quantization (VQ) strategy that combines estimation and compression in one efficient step. The idea is to train a VQ on pairs of noisy and clean images. When presented with a noisy image, our VQ-based system estimates the noise variance and then performs joint denoising and compression. Simulations performed on images corrupted by additive, white, Gaussian noise show significant denoising at various bit rates. Results also indicate that our system is robust enough to handle a wide range of noise variations, while designed for a particular noise variance.
The Joint Photographic Experts Group (JPEG) within the ISO international standards organization is defining a new standard for still image compression--JPEG-2000. This paper describes the Wavelet Trellis Coded Quantization (WTCQ) algorithm submitted by SAIC and The University of Arizona to the JPEG-2000 standardization activity. WTCQ is the basis of the current Verification Model being used by JPEG participants to conduct algorithm experiments. The outcomes from these experiments will lead to the ultimate specification of the JPEG-2000 algorithm. Prior to describing WTCQ and its subsequent evolution into the initial JPEG-2000 VM, a brief overview of the objectives of JPEG-2000 and the process by which it is being developed is presented.
A system is presented for compression of hyperspectral imagery which utilizes trellis coded quantization (TCQ). Specifically, TCQ is used to encode transform coefficients resulting from the application of an 8X8X8 discrete cosine transform. Side information and rate allocation strategies are discussed. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This entropy constrained system achieves a compression ratio of greater than 70:1 with an average PSNR of the coded hyperspectral sequence exceeding 40.5 dB.
The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multiresolution approximations. An image is decomposed into a sequence of orthogonal components, the first being an approximation of the original image at some 'base' resolution. By the addition of successive (orthogonal) 'error' images, approximations of higher resolution are obtained. Trellis coded quantization (TCQ) is known as an effective scheme for quantizing memoryless sources with low to moderate complexity. The TCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. In this work, we investigate the use of entropy-constrained TCQ for encoding wavelet coefficients at different bit rates. The lowest-resolution sub-image is quantized using a 2-D discrete cosine transform encoder. For encoding the 512 X 512, 8- bit, monochrome 'Lenna' image, a PSNR of 39.00 dB is obtained at an average bit rate of 0.89 bits/pixel.