Augmenting Vector Quantization with Interval Arithmetics
for Image-coding Applications 

Sandro Ridella, Stefano Rovetta, and Rodolfo Zunino



ABSTRACT

Interval Arithmetics (IA) augments the basic Vector-Quantization
(VQ) paradigm for image compression. The reformulated VQ schema
allows prototypes to assume ranges of admissible locations rather
than be clamped to specific space positions. The
image-reconstruction process exploits the resulting degrees of
freedom to make up for the excessive discretization (such as
blockiness) that often affects VQ-based coding. The paper
describes the algorithms for both the training and the run-time
use of IAVQ codebooks; the possibility of data-driven training
endows the proposed methodology with the flexibility and
adaptiveness of standard VQ methods, as confirmed by experimental
results on real images. 



Keywords:
Vector Quantization Interval Arithmetics
Image Coding Neural Networks Cellular Neural Networks
Analog Circuits & Systems Image Filtering Deblocking