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基于自组织神经网络的纹理图像压缩算法。

Texture Image Compression Algorithm Based on Self-Organizing Neural Network.

机构信息

School of Computer Engineering, Henan Economic and Trade Vocational College, Zhengzhou, Henan 450046, China.

出版信息

Comput Intell Neurosci. 2022 Apr 10;2022:4865808. doi: 10.1155/2022/4865808. eCollection 2022.

DOI:10.1155/2022/4865808
PMID:35440945
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9013571/
Abstract

With the rapid development of science and technology, human beings have gradually stepped into a brand-new digital era. Virtual reality technology has brought people an immersive experience. In order to enable users to get a better virtual reality experience, the pictures produced by virtual skillfully must be realistic enough and support users' real-time interaction. So interactive real-time photorealistic rendering becomes the focus of research. Texture mapping is a technology proposed to solve the contradiction between real time and reality. It has been widely studied and used since it was proposed. However, due to limited bandwidth and memory storage, it brings challenges to the stain dyeing of many large texture images, so texture compression is introduced. Texture compression can improve the utilization rate of cache but also greatly reduce the pressure on data transmission caused by the system, which largely solves the problem of real-time rendering of realistic graphics. Due to the particularity of texture image compression, it is necessary to consider not only the quality of texture image after compression ratio and decompression but also whether the algorithm is compatible with mainstream graphics cards. On this basis, we put forward the texture image compression method based on self-organizing mapping, the experiment results show that our method has achieved good results, and it is superior to other methods in most performance indexes.

摘要

随着科学技术的飞速发展,人类逐渐步入全新的数字时代。虚拟现实技术为人们带来了身临其境的体验。为了使用户获得更好的虚拟现实体验,虚拟技术生成的图像必须足够逼真,并支持用户的实时交互。因此,交互式实时真实感渲染成为研究的焦点。纹理映射是为解决实时性和真实性之间的矛盾而提出的一项技术。自提出以来,它得到了广泛的研究和应用。但是,由于带宽和内存存储有限,它给许多大型纹理图像的污渍染色带来了挑战,因此引入了纹理压缩。纹理压缩可以提高缓存的利用率,但也大大减轻了系统数据传输带来的压力,在很大程度上解决了真实感图形的实时渲染问题。由于纹理图像压缩的特殊性,不仅要考虑压缩比和解压缩后纹理图像的质量,还要考虑算法是否与主流显卡兼容。在此基础上,我们提出了基于自组织映射的纹理图像压缩方法,实验结果表明,我们的方法取得了良好的效果,在大多数性能指标上都优于其他方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/b16673dd5176/CIN2022-4865808.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/b9c40d7c3347/CIN2022-4865808.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/7de2944e4470/CIN2022-4865808.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/2c85a6bc4e57/CIN2022-4865808.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/87a2afcd5117/CIN2022-4865808.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/3daa9853e0cd/CIN2022-4865808.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/5b7331a3f8f8/CIN2022-4865808.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/493fe9e4dc30/CIN2022-4865808.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/1f2ce32b953f/CIN2022-4865808.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/b16673dd5176/CIN2022-4865808.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/b9c40d7c3347/CIN2022-4865808.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/7de2944e4470/CIN2022-4865808.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/2c85a6bc4e57/CIN2022-4865808.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/87a2afcd5117/CIN2022-4865808.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/3daa9853e0cd/CIN2022-4865808.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/5b7331a3f8f8/CIN2022-4865808.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/493fe9e4dc30/CIN2022-4865808.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/1f2ce32b953f/CIN2022-4865808.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f4/9013571/b16673dd5176/CIN2022-4865808.009.jpg

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