Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ)

TL;DR

This paper introduces MGBVQ, a novel image coding method that decomposes pixel correlations into long- and short-range components, using multi-grid architecture and vector quantizers for efficient, low-complexity compression with progressive bitstreams.

Contribution

The paper proposes a new multi-grid multi-block-size vector quantization approach that effectively removes pixel correlations and achieves competitive rate-distortion performance with lower complexity.

Findings

MGBVQ achieves rate-distortion performance comparable to existing coders.

It offers lower computational complexity compared to traditional methods.

Provides a progressive bitstream for scalable image decoding.

Abstract

A multi-grid multi-block-size vector quantization (MGBVQ) method is proposed for image coding in this work. The fundamental idea of image coding is to remove correlations among pixels before quantization and entropy coding, e.g., the discrete cosine transform (DCT) and intra predictions, adopted by modern image coding standards. We present a new method to remove pixel correlations. First, by decomposing correlations into long- and short-range correlations, we represent long-range correlations in coarser grids due to their smoothness, thus leading to a multi-grid (MG) coding architecture. Second, we show that short-range correlations can be effectively coded by a suite of vector quantizers (VQs). Along this line, we argue the effectiveness of VQs of very large block sizes and present a convenient way to implement them. It is shown by experimental results that MGBVQ offers excellent rate-distortion (RD) performance, which is comparable with existing image coders, at much lower complexity. Besides, it provides a progressive coded bitstream.