Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ)
This work addresses image coding efficiency for applications requiring low complexity, though it appears incremental as it builds on existing correlation removal techniques.
The authors tackled image compression by proposing a multi-grid multi-block-size vector quantization (MGBVQ) method to remove pixel correlations, achieving rate-distortion performance comparable to existing coders with much lower complexity.
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.