Learning a Deep Vector Quantization Network for Image Compression

Vector Quantization for Image Compression-Concept Quantization

Learning Tree-Structured Vector Quantization for Image Compression

Kohonen's self-organizing feature map (KSOFM) is an adaptive vector quantization (VQ) scheme for progressive code vector update. However, KSOFM approach belongs to unconstrained vector quantization, which suuers from exponential growth of the codebook. In this paper, a learning tree-structured vector quantization (LTSVQ) is presented for overcoming this drawback, which is based on competitive l...

Vector Quantization Based Image Compression

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate sub bands. These sub bands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. Lloyd extended scalar quantization technique is used to design memory less vect...

Modified Vector Quantization Method for Image Compression

A low bit rate still image compression scheme by compressing the indices of Vector Quantization (VQ) and generating residual codebook is proposed. The indices of VQ are compressed by exploiting correlation among image blocks, which reduces the bit per index. A residual codebook similar to VQ codebook is generated that represents the distortion produced in VQ. Using this residual codebook the di...

Pyramid Vector Quantization for Deep Learning

This paper explores the use of Pyramid Vector Quantization (PVQ) to reduce the computational cost for a variety of neural networks (NNs) while, at the same time, compressing the weights that describe them. This is based on the fact that the dot product between an N dimensional vector of real numbers and an N dimensional PVQ vector can be calculated with only additions and subtractions and one m...