Compressive Sensing Multi-Layer Residual Coefficients for Image Coding
Compressive Sensing Multi-Layer Residual Coefficients for Image Coding
复制标题
用于图像编码的压缩感知多层残差系数
DOI：
10.1109/tcsvt.2019.2898908
复制
发表时间：
2020-04-01
影响因子：
8.4
通讯作者：
Wang, Shidong
中科院分区：
文献类型：
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作者：
Chen, Zan;Hou, Xingsong;Wang, Shidong
基于压缩感知（CS）的图像编码方案已经得到了广泛的研究，但与传统的图像编码技术相比，其率失真性能仍然很差。在本文中，我们提出了CS多层残差编码方案，以纠正这个问题在一定程度上。通过将CS测量值划分为多层并利用其所有先前层的测量值预测特定层的测量值，我们可以将CS测量值转换为更容易压缩的多层残差系数。通过计算量化的地面真实CS测量与其对应的量化的推断测量之间的残差，并且使用霍夫曼编码将每个残差量化索引与二进制码相关联，我们可以有效地减少CS测量之间的冗余。此外，预测和量化过程被设计成与层无关的，这可以节省大量的编码时间。所提出的方法引入了一种新的框架，用于在压缩域中使用CS。实验结果表明，该方法在部分测试图像上的性能明显优于JPEG 2000，接近或达到HEVC-Intra的性能。
Compressive sensing (CS)-based image coding scheme has been enthusiastically studied, but it still has a poor rate-distortion performance compared with the traditional image coding techniques. In this paper, we propose a CS multi-layer residual coding scheme to rectify this problem to a certain extent. By dividing CS measurements into multi-layers and predicting a particular layer’s measurements with all its preceding layers’ measurements, we can transform CS measurements into multi-layer residual coefficients, which are easier to compress. By calculating the residual between the quantized ground-truth CS measurements and their corresponding quantized inference measurements and using Huffman coding to associate each residual quantization index with a binary code, we can reduce the redundancies among CS measurements efficiently. Besides, the prediction and quantization process is designed to be layer-independent, which can save much of the encoding time. The proposed approach introduces a novel framework for using CS in the compression domain. The experimental results show that the proposed scheme can significantly outperform JPEG2000 and approach or reach the performance of HEVC-Intra on some test images.