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Compressive Sensing Multi-layer Residual Coefficients for Image Coding
IEEE Transactions on Circuits and Systems for Video Technology ( IF 8.4 ) Pub Date : 2020-04-01 , DOI: 10.1109/tcsvt.2019.2898908
Zan Chen , Xingsong Hou , Ling Shao , Chen Gong , Xueming Qian , Yuan Huang , Shidong Wang

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.

中文翻译:

用于图像编码的压缩感知多层残差系数

基于压缩感知(CS)的图像编码方案得到了热烈的研究,但与传统的图像编码技术相比,它的率失真性能仍然很差。在本文中,我们提出了一种 CS 多层残差编码方案来在一定程度上纠正这个问题。通过将 CS 测量划分为多层并使用其所有先前层的测量来预测特定层的测量,我们可以将 CS 测量转换为更容易压缩的多层残差系数。通过计算量化地面实况 CS 测量与其对应的量化推理测量之间的残差,并使用霍夫曼编码将每个残差量化索引与二进制代码相关联,我们可以有效地减少 CS 测量之间的冗余。除了,预测和量化过程被设计为与层无关的,这样可以节省大量的编码时间。所提出的方法引入了一种在压缩域中使用 CS 的新框架。实验结果表明,所提出的方案可以显着优于JPEG2000,并且在一些测试图像上接近或达到HEVC-Intra的性能。
更新日期:2020-04-01
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