Side match and overlap match vector quantizers for images

Abstract

A class of vector quantizers with memory that are known as finite state vector quantizers (FSVQs) in the image coding framework is investigated. Two FSVQ designs, namely side match vector quantizers (SMVQs) and overlap match vector quantizers (OMVQs), are introduced. These designs take advantage of the 2-D spatial contiguity of pixel vectors as well as the high spatial correlation of pixels in typical gray-level images. SMVQ and OMVQ try to minimize the granular noise that causes visible pixel block boundaries in ordinary VQ. For 512 by 512 gray-level images, SMVQ and OMVQ can achieve communication quality reproduction at an average of 1/2 b/pixel per image frame, and acceptable quality reproduction. Because block boundaries are less visible, the perceived improvement in quality over ordinary VQ is even greater. Owing to the structure of SMVQ and OMVQ, simple variable length noiseless codes can achieve as much as 60% bit rate reduction over fixed-length noiseless codes.

Rapid Generation of the State Codebook in Side Match Vector Quantization

“…However, VQ encodes each block independently and thus tends to cause visible boundaries between neighboring blocks. Side match vector quantization (SMVQ) is an extension of the VQ [5]. It has been designed to enhance the visual quality of VQ by reducing the visible boundaries.…”

A New Side-Match Finite-State Vector Quantization Using Neural Networks for Image Coding

“…Hence, the encoding time can be reduced, and the image quality can be maintained at the lower bite rate. Moreover, side-match finite-state vector quantization (SMVQ) [13] is a popular class of FSVQs. The basic idea of SMVQ is to predict the current encoding vector by exploiting the correlations of encoded upper and left vectors for the current input vector.…”

ECG compression using multiscale recurrent patterns with period normalization

“…This happens because the calculated distortion, used to evaluate the cost of the node n j , is independent of the representations chosen for the other neighbor segments. In [7], it was proposed an efficient method for controlling the smoothness of representations produced by vector quantization schemes, called side-match vector quantization (SM-VQ). The SM-VQ can be incorporated to MMP to improve its performance for smooth input signals [8].…”

“…When reconstructing the original signal, the decoder needs the original periods as side information, which are differentially and arithmetically encoded and added to the header of the compressed file. The original periods can then be recovered using the same transformation described in (7).…”

“…Generally, cubic splines are preferred because of their ability to fit C2-continuous cubic kernels [9]. A possible improvement to the interpolation method could be to use a point distribution based on the signal characteristics instead of the fixed linear distribution presented in (7).…”