Lossy Image Compression with Stochastic Quantization

Anton Kozyriev, Vladimir Norkin
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Abstract

Lossy image compression algorithms play a crucial role in various domains, including graphics, and image processing. As image information density increases, so do the resources required for processing and transmission. One of the most prominent approaches to address this challenge is color quantization, proposed by Orchard et al. (1991). This technique optimally maps each pixel of an image to a color from a limited palette, maintaining image resolution while significantly reducing information content. Color quantization can be interpreted as a clustering problem (Krishna et al. (1997), Wan (2019)), where image pixels are represented in a three-dimensional space, with each axis corresponding to the intensity of an RGB channel. However, scaling of traditional algorithms like K-Means can be challenging for large data, such as modern images with millions of colors. This paper reframes color quantization as a three-dimensional stochastic transportation problem between the set of image pixels and an optimal color palette, where the number of colors is a predefined hyperparameter. We employ Stochastic Quantization (SQ) with a seeding technique proposed by Arthur et al. (2007) to enhance the scalability of color quantization. This method introduces a probabilistic element to the quantization process, potentially improving efficiency and adaptability to diverse image characteristics. To demonstrate the efficiency of our approach, we present experimental results using images from the ImageNet dataset. These experiments illustrate the performance of our Stochastic Quantization method in terms of compression quality, computational efficiency, and scalability compared to traditional color quantization techniques.
利用随机量化技术压缩有损图像
有损图像压缩算法在图形和图像处理等多个领域发挥着重要作用。随着图像信息密度的增加,处理和传输所需的资源也在增加。Orchard 等人(1991 年)提出的色彩量化技术是应对这一挑战的最主要方法之一。这种技术将图像的每个像素从有限的调色板中最优化地映射成一种颜色,在保持图像分辨率的同时大大减少了信息含量。颜色量化可以被解释为一个聚类问题(Krishna 等人(1997),Wan(2019)),其中图像像素在三维空间中表示,每个轴对应一个 RGB 通道的强度。然而,像 K-Means 这样的传统算法的扩展对于大型数据(如具有数百万种颜色的现代图像)来说具有挑战性。本文将颜色量化重构为图像像素集与最优调色板之间的三维随机运输问题,其中颜色的数量是一个已定义的超参数。我们采用亚瑟等人(2007 年)提出的播种技术随机量化(SQ)来增强色彩量化的可扩展性。这种方法在量化过程中引入了概率元素,可能会提高效率和对不同图像特征的适应性。为了证明我们方法的效率,我们使用 ImageNet 数据集中的图像给出了实验结果。与传统的颜色量化技术相比,这些实验说明了我们的随机量化方法在压缩质量、计算效率和可扩展性方面的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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