926 research outputs found
Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding
Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties
High-throughput variable-to-fixed entropy codec using selective, stochastic code forests
Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto et al.\u27s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds
Wavelet Based Image Coding Schemes : A Recent Survey
A variety of new and powerful algorithms have been developed for image
compression over the years. Among them the wavelet-based image compression
schemes have gained much popularity due to their overlapping nature which
reduces the blocking artifacts that are common phenomena in JPEG compression
and multiresolution character which leads to superior energy compaction with
high quality reconstructed images. This paper provides a detailed survey on
some of the popular wavelet coding techniques such as the Embedded Zerotree
Wavelet (EZW) coding, Set Partitioning in Hierarchical Tree (SPIHT) coding, the
Set Partitioned Embedded Block (SPECK) Coder, and the Embedded Block Coding
with Optimized Truncation (EBCOT) algorithm. Other wavelet-based coding
techniques like the Wavelet Difference Reduction (WDR) and the Adaptive Scanned
Wavelet Difference Reduction (ASWDR) algorithms, the Space Frequency
Quantization (SFQ) algorithm, the Embedded Predictive Wavelet Image Coder
(EPWIC), Compression with Reversible Embedded Wavelet (CREW), the Stack-Run
(SR) coding and the recent Geometric Wavelet (GW) coding are also discussed.
Based on the review, recommendations and discussions are presented for
algorithm development and implementation.Comment: 18 pages, 7 figures, journa
Data compression techniques applied to high resolution high frame rate video technology
An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended
1994 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
Wavelet-Based Embedded Rate Scalable Still Image Coders: A review
Embedded scalable image coding algorithms based on the wavelet transform have received considerable attention lately in academia and in industry in terms of both coding algorithms and standards activity. In addition to providing a very good coding performance, the embedded coder has the property that the bit stream can be truncated at any point and still decodes a reasonably good image. In this paper we present some state-of-the-art wavelet-based embedded rate scalable still image coders. In addition, the JPEG2000 still image compression standard is presented.
Underwater radio frequency image sensor using progressive image compression and region of interest
The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement; however, the limited and varying bandwidth of underwater radio frequency (RF) channels is a major obstacle for the operator to get camera feedback and supervise the intervention. This paper proposes the use of progressive (embedded) image compression and region of interest (ROI) for the design of an underwater image sensor to be installed in an autonomous underwater vehicle, specially when there are constraints on the available bandwidth, allowing a more agile data exchange between the vehicle and a human operator supervising the underwater intervention. The operator can dynamically decide the size, quality, frame rate, or resolution of the received images so that the available bandwidth is utilized to its fullest potential and with the required minimum latency. The paper focuses first on the description of the system, which uses a camera, an embedded Linux system, and an RF emitter installed in an OpenROV housing cylinder. The RF receiver is connected to a computer on the user side, which controls the camera monitoring parameters, including the compression inputs, such as region of interest (ROI), size of the image, and frame rate. The paper focuses on the compression subsystem and does not attempt to improve the communications physical media for better underwater RF links. Instead, it proposes a unified system that uses well-integrated modules (compression and transmission) to provide the scientific community with a higher-level protocol for image compression and transmission in sub-sea robotic interventions
A User Oriented Image Retrieval System using Halftoning BBTC
The objective of this paper is to develop a system for content based image retrieval (CBIR) by accomplishing the benefits of low complexity Ordered Dither Block Truncation Coding based on half toning technique for the generation of image content descriptor. In the encoding step ODBTC compresses an image block into corresponding quantizes and bitmap image. Two image features are proposed to index an image namely co-occurrence features and bitmap patterns which are generated using ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two quantizes and bitmap respectively by including visual codebooks. The proposed system based on block truncation coding image retrieval method is not only convenient for an image compression but it also satisfy the demands of users by offering effective descriptor to index images in CBIR system
Sparse representation based hyperspectral image compression and classification
Abstract
This thesis presents a research work on applying sparse representation to lossy hyperspectral image
compression and hyperspectral image classification. The proposed lossy hyperspectral image
compression framework introduces two types of dictionaries distinguished by the terms sparse
representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively.
The former is learnt in the spectral domain to exploit the spectral correlations, and the
latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in
hyperspectral images. To alleviate the computational demand of dictionary learning, either a
base dictionary trained offline or an update of the base dictionary is employed in the compression
framework. The proposed compression method is evaluated in terms of different objective
metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including
JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of
both SRSD and MSSD approaches.
For the proposed hyperspectral image classification method, we utilize the sparse coefficients
for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular,
the discriminative character of the sparse coefficients is enhanced by incorporating contextual
information using local mean filters. The classification performance is evaluated and compared
to a number of similar or representative methods. The results show that our approach could outperform
other approaches based on SVM or sparse representation.
This thesis makes the following contributions. It provides a relatively thorough investigation
of applying sparse representation to lossy hyperspectral image compression. Specifically,
it reveals the effectiveness of sparse representation for the exploitation of spectral correlations
in hyperspectral images. In addition, we have shown that the discriminative character of sparse
coefficients can lead to superior performance in hyperspectral image classification.EM201
- …