{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:49:47Z","timestamp":1787028587500,"version":"build-2736575974"},"reference-count":113,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:00:00Z","timestamp":1706227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP22K12101"],"award-info":[{"award-number":["JP22K12101"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. This paper provides a comprehensive exploration of the key components within the domain of CS applied to image and video compression. We delve into the fundamental principles of CS, highlighting its ability to efficiently capture and represent sparse signals. The sampling strategies employed in image compression applications are examined, emphasizing the role of CS in optimizing the acquisition of visual data. The measurement coding techniques leveraging the sparsity of signals are discussed, showcasing their impact on reducing data redundancy and storage requirements. Reconstruction algorithms play a pivotal role in CS, and this article reviews state-of-the-art methods, ensuring a high-fidelity reconstruction of visual information. Additionally, we explore the intricate optimization between the CS encoder and decoder, shedding light on advancements that enhance the efficiency and performance of compression techniques in different scenarios. Through a comprehensive analysis of these components, this review aims to provide a holistic understanding of the applications, challenges, and potential optimizations in employing CS for image and video compression tasks.<\/jats:p>","DOI":"10.3390\/info15020075","type":"journal-article","created":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T10:54:27Z","timestamp":1706266467000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Compressive Sensing in Image\/Video Compression: Sampling, Coding, Reconstruction, and Codec Optimization"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-0522","authenticated-orcid":false,"given":"Jinjia","family":"Zhou","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Hosei University, Tokyo 184-8584, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8858-4378","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Hosei University, Tokyo 184-8584, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. 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