摘要翻译：
本文提出了一种基于拉链变换(ZT)和逆拉链变换(iZT)的近无损图像压缩解压方案。提出的ZT利用了离散傅里叶变换(DFT)的共轭对称性。提出的变换是用两种不同的结构实现的：交错ZT和级联ZT。为了量化所提变换的有效性，我们用离散余弦变换(DCT)和快速Walsh Hadamard变换(FWHT)在无损压缩能力和计算代价方面进行了基准测试。数值仿真结果表明，与DCT和FWHT相比，基于ZT的压缩算法具有更好的压缩性能和更快的实现速度。此外，交错和串联ZT在大多数测试用例中都显示出类似的结果。
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英文标题：
《A Fast and Efficient Near-Lossless Image Compression using Zipper
  Transformation》
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作者：
Babajide O. Ayinde
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Near-lossless image compression-decompression scheme is proposed in this paper using Zipper Transformation (ZT) and inverse zipper transformation (iZT). The proposed ZT exploits the conjugate symmetry property of Discrete Fourier Transformation (DFT). The proposed transformation is implemented using two different configurations: the interlacing and concatenating ZT. In order to quantify the efficacy of the proposed transformation, we benchmark with Discrete Cosine Transformation (DCT) and Fast Walsh Hadamard Transformation (FWHT) in terms of lossless compression capability and computational cost. Numerical simulations show that ZT-based compression algorithm is near-lossless, compresses better, and offers faster implementation than both DCT and FWHT. Also, interlacing and concatenating ZT are shown to yield similar results in most of the test cases considered.
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PDF链接：
https://arxiv.org/pdf/1710.02907