{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:16:26Z","timestamp":1767183386992,"version":"build-2065373602"},"reference-count":102,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of compressed image and video data can greatly reduce input data size and eliminate the need for decompression and recompression, thereby achieving significant savings in memory and computation time. There exists a body of research on compression domain data processing and analysis. This survey focuses on the work related to image and video data. The papers cited are categorized based on their target applications, including image and video resizing and retrieval, information hiding and watermark embedding, image and video enhancement and segmentation, object and motion detection, as well as pattern classification, among several other applications. Key methods used for these applications are explained and discussed. Comparisons are drawn among similar approaches. We then point out possible directions of further research.<\/jats:p>","DOI":"10.3390\/info14030184","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:40:11Z","timestamp":1678934411000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9983-7860","authenticated-orcid":false,"given":"Yuhang","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, 301 Sparkman Dr, Huntsville, AL 35899, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7265-2188","authenticated-orcid":false,"given":"W. David","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, 301 Sparkman Dr, Huntsville, AL 35899, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"ref_1","unstructured":"Paula Dootson (2023, March 14). 3.2 Billion Images and 720,000 Hours of Video Are Shared Online Daily. Can You Sort Real from Fake?. Available online: https:\/\/www.qut.edu.au\/study\/business\/insights\/3.2-billion-images-and-720000-hours-of-video-are-shared-online-daily.-can-you-sort-real-from-fake."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Antonio, R., Faria, S., Tavora, L.M., Navarro, A., and Assuncao, P. (2022, January 19\u201322). Learning-based compression of visual objects for smart surveillance. 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