基于图像超分辨残差学习网络的高效图像压缩方法

Jianhua Hu, Bo Wang, Xiaolin Liu, Shuzhao Zheng, Zongren Chen, Weimei Wu, Jianding Guo, Woqing Huang
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引用次数: 1

摘要

随着互联网视频图像信息的快速增长,图像数据中存在着大量的冗余。使用较少的数据流信息来传输图像或图像中包含的信息量。其目的是减少图像的冗余,从而以低比特率存储图像,减少数据存储空间。在一般的图像压缩方法中,采用混合编码框架。每一种算法通过特定的设计算法采用固定的算法模式,没有全局优化。图像压缩主要分为预测、变换、量化、数字熵编码等步骤。目前,基于深度学习技术的超分辨率网络研究有很多。其主要功能是重建高分辨率图像,替代图像放大低分辨率图像,如线性插值等,这对提高图像分辨率、降噪、去模糊等性能有很大的帮助,但目前还没有有效的方法利用超分辨率网络应用来提高压缩重建图像的质量。本文提出了一种利用图像超分辨率残差学习网络来提高压缩图像质量的新方法,该方法将压缩后的图像编码为内容流,并将传输的相应参数编码为模型流。首先将原始图像缩小到源图像的1/2大小,然后使用现有的编解码器将小图像编码成内容流。其次,利用残差学习超分辨率(SR)网络进行图像滤波,利用解码图像大小调整方法对重构图像进行缩放,提高图像边缘特征提取的质量;我们的研究结果表明,h265在低分辨率重构图像(每像素比特数小于0.1)中有显著的性能改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient image compression method using image super-resolution residual learning network
With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).
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