论文标题
轻量级图像编解码器通过多网格多块大小矢量量化（MGBVQ）
Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ)
论文作者
论文摘要
提出了一种多网格多块大小矢量量化（MGBVQ）方法，用于在这项工作中进行图像编码。图像编码的基本概念是在量化和熵编码之前删除像素之间的相关性，例如，由现代图像编码标准所采用的离散余弦变换（DCT）和内部预测。我们提出了一种删除像素相关性的新方法。首先，通过将相关性分解为长期和短距离相关性，我们由于其平滑度而代表了较粗的网格中的远距离相关性，从而导致了多机格里德（MG）编码体系结构。其次，我们表明可以通过一组矢量量化器（VQS）有效地编码短程相关性。沿着这条线，我们争论了非常大的块大小的VQ的有效性，并提出了一种实施它们的便捷方法。实验结果表明，MGBVQ提供了出色的速率 - 延伸性能（RD）性能，与现有的图像编码器相当，复杂性较低。此外，它提供了渐进的编码bitstream。
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.
