{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:44:49Z","timestamp":1747190689669,"version":"3.40.5"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,5,11]]},"abstract":"<jats:p>Lossy compression can produce false information, such as blockiness, noise, ringing, ghosting, aliasing, and blurring. This paper provides a comprehensive model for optical remote sensing image characteristics based on the block standard deviation\u2019s retention rate (BSV). We first propose a compression evaluation method, CR_CI, that combines neural network prediction and remote sensing image quality fidelity. Through the compression evaluation and improved experimental verification of multiple satellites (CBERS-02B satellite, ZY-1-02C satellite, CBERS-04 satellite, GF-1, GF-2, etc.), CR_CI can be stable, cleverly test changes in the information extraction performance of optical remote sensing images, and provide strong support for optimizing the design of compression schemes. In addition, a predictor of remote sensing image number compression is constructed based on deep neural networks, which combines compression efficiency (compression ratio), image quality, and protection. Empirical results demonstrate the image\u2019s highest compression efficiency under the premise of satisfying visual interpretation and quantitative application.<\/jats:p>","DOI":"10.1155\/2021\/9948811","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T20:20:51Z","timestamp":1620850851000},"page":"1-9","source":"Crossref","is-referenced-by-count":1,"title":["Remote Sensing Image Compression Evaluation Method Based on Neural Network Prediction and Fusion Quality Fidelity"],"prefix":"10.1155","volume":"2021","author":[{"given":"Wenbing","family":"Yang","sequence":"first","affiliation":[{"name":"Yiwu Industrial and Commercial College, Yiwu, Zhejiang 322000, China"}]},{"given":"Feng","family":"Tong","sequence":"additional","affiliation":[{"name":"Wenzhou Heshun Packaging Machinery Co.,Ltd., Wenzhou, Zhejiang 325000, China"}]},{"given":"Xiaoqi","family":"Gao","sequence":"additional","affiliation":[{"name":"Zenghe Packaging Co.,Ltd., Wenzhou, Zhejiang 325000, China"}]},{"given":"Chunlei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zenghe Packaging Co.,Ltd., Wenzhou, Zhejiang 325000, China"}]},{"given":"Guantian","family":"Chen","sequence":"additional","affiliation":[{"name":"Zenghe Packaging Co.,Ltd., Wenzhou, Zhejiang 325000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3693-1267","authenticated-orcid":true,"given":"Zhijian","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Digital Engineering, Zhejiang Dongfang Polytechnic, Wenzhou, Zhejiang, China"}]}],"member":"311","reference":[{"key":"1","first-page":"215","volume-title":"Surveillance and Reconnaissance Image Systems (Modeling and Performance Prediction)","author":"L. 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