Status : Bumped Here's few cents of author on series of playlists that appears in collaboration with @unfgws , @unf_gws & @wtf_gws 𝐏𝐮𝐫𝐩𝐨𝐬𝐞 Following series of playlists on this chanell in collaboration with @unf_gws & @unfgws provides insights/snaps of 𝘴𝘰𝘮𝘦 𝘰𝘧 𝘥𝘦𝘭𝘪𝘣𝘦𝘳𝘢𝘵𝘦𝘭𝘺 𝘴𝘦𝘭𝘦𝘤𝘵𝘦𝘥 videos author(@wtf_gws) have gone through , whether entertainment, studies 𝐨𝐫 learning something new . 𝘉𝘢𝘴𝘪𝘤𝘢𝘭𝘭𝘺 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘸𝘢𝘺𝘣𝘢𝘤𝘬 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘭𝘺 𝘧𝘰𝘳 themselves. 𝙉𝙤𝙩𝙚 1) These videos do not represent creators'/authors' personal/political opinions , neither do they account for information provided in videos. These playlists are meant for entertainment purpose as a medium to be able to look back in their history . 2) Some selections are deliberately put to make it humorous, and does not represent all of what is being watched by them . ◦•●◉✿ Rules ✿◉●•◦ a) For earlier playlists : They have scraped the videos in thier liked/saved playlist. and have tried arranging them in order they were watched. b) Newer playlists are being made by directly saving videos in it. c) Each playlist shall be bumped after 100 episodes/videos. ◦•●◉✿ History ✿◉●•◦ a) Starting point of it as they remember was 2021. Thus playlist 'A' [wtf gws] mostly has videos watched stretching from 2021 to 2025 . b) Playlist 'B' [unfgws] has videos stretching from 2024 -2026. c) The idea of creating such playlist is from 2025, and since then ,they have planned it. ▀▄▀▄ 𝐂𝐔𝐑𝐑𝐄𝐍𝐓 ▄▀▄▀ Playlist 'B' : This playlist captures some of snaps from 2024 to 2026. Again not complete picture, neither any videos represents any affiliation with author. [description incomplete yet]
Curated by: wtf gws (100 videos)
In this interview, Jan Ozer speaks with Dr. Elena Alshina, the Audio Visual Lab Director and Director of the Media Codec and Standardization Lab at Huawei. Dr. Alshina discusses the next generation of JPEG standards, JPEG AI, an advanced image compression technology driven by neural networks. She elaborates on its development, design principles, performance metrics, and potential applications in various fields, including super-resolution, noise reduction, and image classification. Key Video Content: • Introduction and Background of JPEG AI • 00:00:00 - Introduction to JPEG AI • 00:00:55 - Dr. Alshina’s background and career • 00:01:08 - Overview of the JPEG AI project • Technical Aspects and Performance • 00:01:41 - Neural network-based image compression • 00:02:10 - Super-resolution and noise reduction • 00:03:14 - Performance metrics and image classification • Development and Standards • 00:04:15 - Comparison with classical codecs • 00:05:06 - Functionality for humans and machines • 00:06:05 - Quality metrics and human perception • 00:09:19 - Bitrate and encoding complexities • Applications and Implementations • 00:10:29 - Real-world application scenarios • 00:12:00 - NPU and GPU usage in smartphones • 00:15:00 - Decoding capabilities and hardware requirements • 00:20:25 - Interoperability and device testing • Future Prospects and Use Cases • 00:27:22 - Future developments in JPEG AI standards • 00:31:27 - Machine vision and video coding potential • 00:33:08 - Questions on video applicability and new codecs Conclusion: Dr. Alshina's description reveals a promising future for JPEG AI. The technology's adaptability for both human and machine use, along with its increased performance, reduced complexity, and faster encoding times, underscores its potential to revolutionize digital imaging standards and applications.