Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
Wavelets and subband coding
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Digital Pictures: Representation and Compression
Digital Pictures: Representation and Compression
Lattice vector quantization of generalized Gaussian sources
IEEE Transactions on Information Theory
Bennett's integral for vector quantizers
IEEE Transactions on Information Theory
Vector quantization of image subbands: a survey
IEEE Transactions on Image Processing
An entropy-coded lattice vector quantizer for transform and subband image coding
IEEE Transactions on Image Processing
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
Cluster-based probability model and its application to image and texture processing
IEEE Transactions on Image Processing
Image-adaptive vector quantization in an entropy-constrained framework
IEEE Transactions on Image Processing
On the computational complexity of the LBG and PNN algorithms
IEEE Transactions on Image Processing
Additive vector decoding of transform coded images
IEEE Transactions on Image Processing
On the modeling of DCT and subband image data for compression
IEEE Transactions on Image Processing
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A new paradigm that combines data modeling and vector quantization in an effective coding technique is presented. We fit a statistical model to the input data and use the best fit parameters to synthesize training vector sets with statistics similar to the input. By knowing the best-fit parameters, the decoder can synthesize the same training sets, while identical codebooks are obtained at both encoder and decoder based on the same codebook generation procedure. As a result, complete codebook adaptation is achieved with a very small increase in the bit rate. The implementation of the new technique in the transform domain produced competitive results when compared to other methods relying on vector quantization and transform coding. In particular, the image Lena was coded at 0.28 bits/pixel with a peak signal-to-noise ratio of 32.51 dB.