Image interpolation model based on packet losing network
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Image interpolation model based on packet losing network Changjiang Jiang 1 & Hantao Li 1 & Shangbo Zhou 1,2 & Zihan Zhang 2 & Jim Yu 1 & Long Chen 1 & Xianzhong Xie 1,3 Received: 20 June 2019 / Revised: 13 May 2020 / Accepted: 24 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, a method combining an error hiding algorithm with image super-resolution reconstruction is proposed, which uses packet loss compensation as an alternative to traditional reconstruction processes. Our algorithm provides an alternative to traditional frameworks by firstly estimating the edge direction of the block and then interpolating along the edge direction. We define a pixel span function to obtain the missing image details, and the pixels are reconstructed using this function to detect their possible textures. Experiments using established image data sets show that comparison with seven other classical image interpolation algorithms, the proposed approach achieves both higher quantitative and qualitative performance results, ultimately providing better visual effects. Keywords Image super-resolution . Packet loss . Packet loss compensation . Interpolation . Error hiding . Pixel span
1 Introduction With the rapid development of data compression, network, and communication technologies, multimedia applications combining images, video, audio, and data are becoming increasingly prominent in social life. In the era of an information knowledge explosion, images and videos * Shangbo Zhou [email protected]
1
College of Automation, Chongqing University of Posts and Telecommunications, Nan’an District, Chongqing 400065, China
2
College of Computer Science, Chongqing University, Chongqing 400044, China
3
Chongqing Key Lab of Computer Network and Communication Technology, Chongqing 400065, China
Multimedia Tools and Applications
have attracted people’s attention for their intuitive, vivid, and practical characteristics. These requirements have accelerated research on techniques that quickly obtain high-quality (HD) images or videos. Image super-resolution reconstruction is a feasible and effective technique to solve the above problems. Super-resolution image reconstruction (SRIR or SR) refers to the use of signal processing and image processing methods to convert existing low-resolution (LR) images into high-resolution (HR) images, which enrich the details of the image and achieve better visual effects. It is widely used in video surveillance, image printing, criminal investigation analysis, medical image processing, satellite imaging, and other fields. In recent years, researchers have made significant contributions to SR technologies. The core concept is to use redundant and complementary information between LR image sequences and combine relevant prior information to improve the reconstruction process. Ultimately, a reconstructed image is found by solving the objective function. According to different applications, the algorithm of SR technology can be divided into two categories
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