{ "$schema": "/api/schemas/documents.schema.json", "apiVersion": "1.1.0", "lastModified": "2026-10-15T10:03:40.282Z", "sourcePath": "src/main/data/docs", "docId": "10.5594-JMI.2022.3160541", "document": { "abbrevTitle": "SMPTE Mot. Imag. J", "abbrevTitle$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "abstract": "The demand for video through over-the-top (OTT) has been constantly increasing in recent years. During the COVID-19 pandemic, demand skyrocketed, hence leading to the need for better video compression. The human visual system (HVS) can quickly select visually important regions in its visual field. These regions are captured at high resolution, while other peripheral regions receive little attention. Saliency maps are a way to imitate the HVS attention mechanism. Recently, deep learning-based saliency models have achieved tremendous improvements. This article leverages state-of-the-art deep learning-based saliency models to improve video coding efficiency. First, a saliency-based rate control scheme is integrated in a high-efficiency video encoder (HEVC). Then, a saliency-guided preprocessing filtering step is introduced. Finally, the two approaches are combined. Objective and subjective evaluations show that it can lower the bitrate from 6% to almost 30% while maintaining the same visual quality.", "abstract$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "authors": [ { "bio": "Sébastien Pelurson received a PhD degree in computer science from the University of Grenoble Alpes, Grenoble, France, in 2016. From 2016 to 2019, he worked in the field of augmented reality, and more specifically on the use of deep learning models to improve 3D tracking algorithms on mobile devices. He joined Ateme, Rennes, France, in 2020. His research interests include video coding optimization based on machine learning technology.", "name": "Sébastien Pelurson", "orcid": "0000-0001-6759-3224" }, { "bio": "Josselin Cozanet received an engineering diploma from École nationale supérieure d’ingénieurs de Caen & Centre de Recherche (ENSICAEN) National Graduate School of Engineering, Caen, Rennes, France, in 2019. He started working at Ateme, Rennes, in 2019, for an internship in the innovation team on artificial intelligence used to improve encoding latency. He is still working on various topics at Ateme, including artificial intelligence applied to video compression and content delivery network intercommunication with open caching.", "name": "Josselin Cozanet" }, { "bio": "Thomas Guionnet is a fellow research engineer at Ateme, Rennes, France, where he currently leads the Innovation team’s research on artificial intelligence applied to video compression. Beyond his work for Ateme, he has also contributed to the International Standards Organization/Motion Picture Experts Group (ISO/MPEG) - International Telecommunication Union-Telecommunication/Video Coding Experts Group (ITU-T/VCEG) - Versatile Video Coding (VVC), HEVC, and HEVC-3D standardization process; he teaches video compression at the ESIR engineering school in Rennes; and he has authored numerous publications including patents, international conference papers, and journal papers. Prior to joining Ateme, he spent ten years at Envivio conducting research on realtime encoding, video preprocessing, and video quality assessment. He holds a PhD from University of Rennes 1, Rennes.", "name": "Thomas Guionnet" }, { "bio": "Mohsen Abdoli received a master of engineering degree from Sharif University of Technology, Tehran, Iran, in 2013, and a PhD jointly from Université Paris-Saclay and CentraleSupélec, Paris, France, in 2018. In 2015, he joined Orange Labs, Rennes, France, where he actively contributed to the development of several video compression techniques, targeting the VVC standard by JVET, particularly in the domain of intraprediction and residual coding of natural/screen content. In 2018, he joined Ateme, Rennes, where he contributed to the world-first implementation of realtime VVC transcoder for video-on-demand and live streaming applications. He is currently with IRT b<> com, Rennes, holding the position of standardization engineer. His research interests include areas of signal processing, encoder optimization, machine learning, and quality assessment.", "name": "Mohsen Abdoli" }, { "bio": "Thibaud Biatek received a PhD degree in signal and image processing from the Institut National des Sciences Appliquées, Rennes, France, in 2016. From 2013 to 2017, he was a doctoral and post-doctoral fellow with TDF, Cesson-Sévigné, France. From 2017 to 2019, he was a video coding expert with TDF, where he was involved in MPEG and DVB standardization activities. In 2019, he was a senior engineer with Qualcomm, San Diego, CA, working on VVC standardization. Since 2020, he has been director of technology and standards at Ateme, working on partnership projects and multimedia standards, contributing to MPEG, DVB, and 3GPP groups. His research interests include compression, processing, and delivery of audiovisual signals over broadcast and broadband networks.", "name": "Thibaud Biatek" } ], "authors$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "contentType": "orig-research", "contentType$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "copyright": { "holder": "SMPTE", "holder$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "year": "2022", "year$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" } }, "copyright$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "docId": "10.5594-JMI.2022.3160541", "docId$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "docLabel": "SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022)", "docLabel$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "docTitle": "AI-Based Saliency-Aware Video Coding", "docTitle$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "docType": "Journal Article", "docType$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "doi": "10.5594/JMI.2022.3160541", "doi$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "href": "https://doi.org/10.5594/JMI.2022.3160541", "href$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "issn": { "electronic": "2160-2492", "electronic$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "print": "1545-0279", "print$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal 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"parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "keywords": [ "Artificial Intelligence", "Saliency", "Video Coding" ], "keywords$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "number": "4", "number$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": "smpte-idams-publication@v1" }, "pages": "21–29", "pages$meta": { "confidence": "high", "note": "Ingested from SMPTE IDAMS (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772090.xml) via idamsIngest.js", "source": "parsed", "updated": "2026-10-07T17:09:33.718Z", "version": 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