Lecture: Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing...
Master data
Title: | Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution |
Description: | In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional approaches and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms as they show a significant gain over traditional approaches. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates a maximum threshold for the quality is set and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method. |
Keywords: | HAS, per-title, deep learning, compression, bitrate ladder |
Type: | Registered lecture |
Homepage: | http://www.vcip2021.org/program/ |
Event: | International Conference on Visual Communications and Image Processing (VCIP 2021) (München) |
Date: | 08.12.2021 |
lecture status: | stattgefunden (Präsenz) |
Participants
Hadi Amirpourazarian (internal) |
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Christian Timmerer (internal) |
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Mohammad Ghanbari (internal) |
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Hannaneh Barahouei Pasandi (external) |
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Virginia Commonwealth University
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US Richmond |
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