A sub‐regional compression method for greenhouse images based on <scp>CNN</scp> image quality assessment

Journal of Food Processing and PreservationEarly View e16992 ORIGINAL ARTICLE A sub-regional compression method for greenhouse images based on CNN image quality assessment Diankun Ma, Diankun Ma China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorHaojie Wen, Haojie Wen China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorXinxing Li, Xinxing Li orcid.org/0000-0001-6007-6902 China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorTianhua Xie, Tianhua Xie China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorXiang Li, Corresponding Author Xiang Li cqlixiang@cau.edu.cn orcid.org/0000-0002-6535-0584 China Agricultural University, Beijing, PR China Correspondence Xiang Li, China Agricultural University, Beijing 100083, PR China. Email: cqlixiang@cau.edu.cnSearch for more papers by this author Diankun Ma, Diankun Ma China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorHaojie Wen, Haojie Wen China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorXinxing Li, Xinxing Li orcid.org/0000-0001-6007-6902 China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorTianhua Xie, Tianhua Xie China Agricultural University, Beijing, PR ChinaSearch for more papers by this authorXiang Li, Corresponding Author Xiang Li cqlixiang@cau.edu.cn orcid.org/0000-0002-6535-0584 China Agricultural University, Beijing, PR China Correspondence Xiang Li, China Agricultural University, Beijing 100083, PR China. Email: cqlixiang@cau.edu.cnSearch for more papers by this author First published: 11 August 2022 https://doi.org/10.1111/jfpp.16992Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract As a major Internet of Things information carrier within the greenhouse, image quality and image file size need to be balanced. To reduce image file size while maintaining image quality, a sub-regional compression (SRC) method is proposed, and its optimal compression quality of the background regions is selected using image quality assessments such as SSIM, BRISQUE, NIQE, and PIQE. In addition, a convolutional neural network-based NIMA model to measure image quality is also being introduced. In the end, the SRC method is compared with JPEG compression default mode and JPEG compression with quality 30 in NIMA score, compression time, and compression efficiency, proving the effectiveness of the method. Practical applications Certain vegetables and fruits, such as cucumbers, tomatoes, etc., in most cases are processed directly into the hands of consumers, so the loss is directly attributable to their growth, especially pests and diseases. Monitoring for the above situation is a great improvement, but the increase in monitoring accuracy brings video/key frame to becoming larger. Therefore, it is better to reduce the file size as well as to ensure clarity in regions of interest, viewing the files faster and detecting the losses by humans or machines as easily as possible. This paper investigates the overall process and innovatively introduces deep learning into image quality assessment to better simulate human perception of images, and the research results of this paper can be applied to different models of surveillance cameras to achieve the same results. CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article. Open Research DATA AVAILABILITY STATEMENT The data underlying this article cannot be shared publicly due to the privacy of greenhouse institutions that participated in the study. The data will be shared on reasonable request to the corresponding author. Early ViewOnline Version of Record before inclusion in an issuee16992 RelatedInformation