417 research outputs found
Self-similarity and wavelet forms for the compression of still image and video data
This thesis is concerned with the methods used to reduce the data volume required to represent
still images and video sequences. The number of disparate still image and video
coding methods increases almost daily. Recently, two new strategies have emerged and
have stimulated widespread research. These are the fractal method and the wavelet transform.
In this thesis, it will be argued that the two methods share a common principle: that
of self-similarity. The two will be related concretely via an image coding algorithm which
combines the two, normally disparate, strategies.
The wavelet transform is an orientation selective transform. It will be shown that the
selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity
while keeping computational cost low. To address this, a new wavelet transform
is presented which allows for greater orientation selectivity, while maintaining the
orthogonality and data volume of the conventional wavelet transform. Many designs for
vector quantizers have been published recently and another is added to the gamut by this
work. The tree structured vector quantizer presented here is on-line and self structuring,
requiring no distinct training phase. Combining these into a still image data compression
system produces results which are among the best that have been published to date.
An extension of the two dimensional wavelet transform to encompass the time dimension
is straightforward and this work attempts to extrapolate some of its properties into three
dimensions. The vector quantizer is then applied to three dimensional image data to
produce a video coding system which, while not optimal, produces very encouraging
results
Image Compression by Wavelet Transform.
Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia-based web applications.
This thesis studies image compression with wavelet transforms. As a necessary background, the basic concepts of graphical image storage and currently used compression algorithms are discussed. The mathematical properties of several types of wavelets, including Haar, Daubechies, and biorthogonal spline wavelets are covered and the Enbedded Zerotree Wavelet (EZW) coding algorithm is introduced. The last part of the thesis analyzes the compression results to compare the wavelet types
Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes
Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy
data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining
the quality
Energy Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains.
This invention relates to representation of one and multidimensional signal vectors in nonorgothonal domains and design of Vector Quantizers that can be chosen among these representations. There is presented a Vector Quantization technique in multiple nonorthogonal domains for both waveform and model based signal characterization. An iterative codebook accuracy enhancement algorithm, applicable to both waveform and model based Vector Quantization in multiple nonorthogonal domains, which yields further improvement in signal coding performance, is disclosed. Further, Vector Quantization in in nonorthogonal domains is applied to speech and exhibits clear performance improvements of reconstruction quality for the same bit rate compared to existing single domain Vector Quantization techniques. The technique disclosed herein can be easily extended to several other one and multidimensional signal classes
Rate-distortion adaptive vector quantization for wavelet imagecoding
We propose a wavelet image coding scheme using rate-distortion adaptive tree-structured residual vector quantization. Wavelet transform coefficient coding is based on the pyramid hierarchy (zero-tree), but rather than determining the zero-tree relation from the coarsest subband to the finest by hard thresholding, the prediction in our scheme is achieved by rate-distortion optimization with adaptive vector quantization on the wavelet coefficients from the finest subband to the coarsest. The proposed method involves only integer operations and can be implemented with very low computational complexity. The preliminary experiments have shown some encouraging results: a PSNR of 30.93 dB is obtained at 0.174 bpp on the test image LENA (512×512
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