Abstract
In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.
Highlights
Data compression techniques, based on the Vector Quantization (VQ), are widely used in several scenarios
We focus on some easy-to-implement design concepts to design concepts to extend theSubsequently, Adaptive Vector Quantization (AVQ) algorithm, in order to allow the lossy compression of image extend the algorithm, in order to allow the lossy compression of image sequences
We propose a lossy compression scheme for image sequences, based on the AVQ
Summary
Data compression techniques, based on the Vector Quantization (VQ), are widely used in several scenarios. By means of the VQ, the input data is subdivided into blocks, denoted as vectors, and each vector is replaced by a similar one (or equal, when possible) stored into a static dictionary of codebook vectors (denoted as codewords) [4]. A VQ-based encoder calculates all the distances (according to a given distortion measure) between the input block (Xk ) and each one of the blocks into the codewords. The size of an index is related to the size of the codewords (denoted as N), since an index is composed by log N bits
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