Image analysis – Image compression or coding – Quantization
Reexamination Certificate
1998-10-30
2003-04-08
Tran, Phuoc (Department: 2621)
Image analysis
Image compression or coding
Quantization
C382S232000, C382S233000, C382S240000, C382S251000, C382S248000, C375S240030, C375S240220
Reexamination Certificate
active
06546146
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to a lossy data compression system using vector quantisation, for reducing data volumes generated by hyper-spectral imaging devices.
BACKGROUND OF THE INVENTION
The next generation of satellite-based remote sensing instruments will produce an unprecedented volume of data. Imaging spectrometers, also known as hyper-spectral imaging devices, are prime examples. They collect image data in hundreds of spectral bands simultaneously from the near ultraviolet through the short wave infrared, and are capable of providing direct identification of surface materials.
Hyper-spectral data thus collected are typically in the form of three-dimensional (3D) data cube. Each data cube has two dimensions in the spatial domain defining a rectangular plane of image pixels, and a third dimension in the spectral domain defining radiance levels of multiple spectral bands per each image pixel. The volume and complexity of hyper-spectral data present a significant challenge to conventional transmission and image analysis methods. The raw data rates for transmitting such data cubes can easily exceed the available downlink capacity or on-board storage capacity of existing satellite systems. Often, therefore, a portion of the data collected on board is discarded before transmission, by reducing the duty cycle, reducing the spatial or spectral resolution, and/or reducing the spatial or spectral range. Obviously, in such cases large amounts of information are lost.
For data processing, a similar problem occurs. In computing, a current trend is toward desktop computers and Internet based communications. Unfortunately, the data cubes require a tremendous amount of storage and, for processing, the storage is preferably random access memory (RAM). Current desktop computers often lack sufficient resources for data processing of data cubes comprising spectral data.
Recent work related to data compression of multi-spectral and hyper-spectral imagery has been reported in the literature, but most of these studies relate to multi-spectral imagery comprised of only a few spectral bands. These prior art systems for multi-spectral imagery yield small compression ratios, usually smaller than 30:1. There are two reasons for this:
1) the prior art systems do not efficiently remove the correlation in the spectral domain, and
2) the redundancy of multi-spectral imagery in the spectral domain is relatively small compared to that of hyper-spectral imagery.
Gen et al teach two systems for hyper-spectral imagery. The first system uses trellis coded quantisation to encode transform coefficients resulting from the application of an 8×8×8 discrete cosine transform. The second system uses differential pulse code modulation to spectrally decorrelate data, while using a 2D discrete cosine transform for spatial decorrelation. These two systems are known to achieve compression ratios of greater than 70:1 in some instance; however, it is desirable to have higher compression ratios with simpler coding structures than those reported in the literature.
In an article entitled “Lossy Compression of Hyperspectral Data Using Vector Quantization” by Michael Ryan and John Arnold in the journal
Remote Sens. Environ.,
Elsevier Science Inc., New York, N.Y., 1997, Vol. 61, pp. 419-436, an overview of general vector quantization techniques as are now known is presented. The article is herein incorporated by reference. In particular, the authors describe issues such as distortion measures and classification issues arising from lossy compression of hyper-spectral data using vector quantization.
Data compression using Vector Quantisation (VQ) has received much attention because of its promise of high compression ratio and relatively simple structure. Unlike scalar quantisation, VQ requires segmentation of the source data into vectors. Commonly, in two-dimensional (2D) image data compression, a block with n×m (n may be equal to m) pixels is taken as a vector, whose length is equal to n×m. Vectors constituted in this way have no physical analogue. Because the blocks are segmented according to row and column indices of an image, the vectors obtained in this manner change at random as the pixel patterns change from block to block. The reconstructed image shows an explicit blocking effect for large compression ratios.
There are several conventional approaches to constituting vectors in a 3D data cube of hyper-spectral imagery. The simplest approach is to treat the 3D data cube as a set of 2D monochromatic images, and segment each monochromatic image into vectors independently as in the 2D-image case. This approach, however, suffers from not taking advantage of the high correlation of data in the spectral domain. There is therefore a need for a data compression system that takes advantage of correlation in the spectral domain and the 2D spatial correlation between adjacent image pixels.
The VQ procedure is known to have two main steps: codebook generation and codevector matching. VQ can be viewed as mapping a large set of vectors into a small subset of indexed codevectors forming a codebook. During encoding, a search through a codebook is performed to find a best codevector to express each input vector. The index or address of the selected codevector in the codebook is stored associated with the input vector or the input vector location. Given two systems having a same codebook, transmission of the index to a decoder over a communication channel from the first system to the second other system allows a decoder within the second other system to retrieve the same codevector from an identical codebook. This is a reconstructed approximation of the corresponding input vector. Compression is thus obtained by transmitting the index of the codevector rather the codevector itself. Many existing algorithms for codebook designs are available, such as the LBG algorithm reported by Linde, Buzo and Gray, the tree-structure codebook algorithm reported by Gray, the self organising feature map reported by Nasrabadi and Feng. Among these, the LBG algorithm is most widely used because of its fidelity. The disadvantages of the LGB algorithm are its complexity and the time burden taken to form the codebook. When the input data is a 3D data cube of hyper-spectral imagery, the processing time can be hundreds of times higher than the normal 2D-image case. There is therefore a need for faster codebook generation algorithms with relatively high fidelity.
It would also be advantageous to provide a method of data compression wherein data compressed according to the method is useful for some forms of data processing, image generation, and data viewing.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a data compression system that allows viewing and analysis of large datastructures without decompression of the entire datastructure. It is yet another object of the invention to provide a data compression algorithm that would allow relatively fast processing of 3D data cubes in a compressed form.
In accordance with the invention there is provided a method of processing hyper-spectral image data defining signal levels of multiple spectral bands in encoded form, the encoded form comprising an array of indices and a codebook comprising a plurality of vectors, the method comprising the steps of:
for each image pixel,
determining a first band from the multiple spectral bands and an index from the array of indices, the index indicative of a vector within the codebook, and
retrieving from the indicated vector within the codebook a value corresponding to the first band; and,
displaying an image comprising image pixels, image pixel values based upon the values retrieved for each image pixel.
In accordance with the invention there is also provided a method of processing hyper-spectral image data defining signal levels of multiple spectral bands per image pixel in encoded form, the encoded form comprising an array of indices and a codebook, the method comprising the step of:
processing spectral vectors
Hollinger Allan B.
Qian Shen-en
Williams Daniel J.
Alavi Amir
Canadian Space Agency
Freedman & Associates
Tran Phuoc
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