Image Compression Using
Vector quantization is an efcient technique used for compressing images. It
is based on the Shannon rate distortion theory, which says that better com-
pression is achieved if samples are coded using vectors instead of scalars.
The nite vectors of pixels are stored in a memory called codebook, which
is used for coding and decoding the images. The image to be compressed is
divided into blocks and called input vectors and are compared with vectors
in memory called codevectors for matches based on some distance criteria.
If the codevector matches the input vector, an index or address of memory
location is stored or transmitted. Because the address has less bits than the
codevector, compression is achieved. The decoding or decompression is the
inverse of encoding. The quality of the reconstructed images depends upon
proper design of the codebook. The algorithms used for the design of vec-
tor quantizers (VQs) (encoder and decoder), such as the oldest and famous
Linde-Buzo-Gray (LBG) algorithm, are discussed in detail. Various types of
VQs such as mean-removed, gain-shape, multistep, and others are presented.
The VQ designs using image transforms such as discrete cosine and wavelet
transforms are illustrated. The use of articial neural networks in VQ design
is also discussed. The performance of all designed codebooks are compared.
In previous chapters, we studied the discrete cosine transform (DCT) and
wavelet-based image compression schemes in detail. The main part of all
lossy compression techniques is the quantizer. Quantization represents ne-
resolution data by a coarse approximation. Therefore, the difference between
ne and coarse resolution is quantization error, which is loss of information
or distortion. In all compression schemes studied so far, scalar quantization
is used. Scalar quantization takes place on a single sample followed by cod-
ing. This process may not involve memory. For example, pulse code modula-
tion (PCM) does not require memory, whereas predictive quantization does.
An important result of Shannon’s rate-distortion theory says that a good
compression ratio is achieved if samples are coded using vectors instead of