JND-aware Two-pass Per-title Encoding Scheme for Adaptive Live Streaming

Menon, Vignesh V; Rajendran, Prajit T; Feldmann, Christian; Schoeffmann, Klaus; Ghanbari, M.; Timmerer, Christian

doi:10.36227/techrxiv.22256704.v1

Cited by 2 publications

(2 citation statements)

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“…Predictive models can comprehensively understand the content complexity and characteristics by extracting relevant spatiotemporal features, such as motion vectors, texture patterns, and frameto-frame differences [21]. In this paper, three DCT-energy-based features [22], the average luma texture energy (𝐸 Y ), the average gradient of the luma texture energy (ℎ), and the average luminescence (𝐿 Y ), for each segment are extracted using open-source Video Complexity Analyzer (VCA) [6,22].…”

Section: Video Complexity Feature Extractionmentioning

confidence: 99%

“…These representations enable a continuous adaptation of the video delivery to the client's network conditions and device capabilities [3]. The increase in the computational complexity using codecs such as High Efficiency Video Coding (HEVC) [4] and Versatile Video Coding (VVC) [5], and improvements in video characteristics such as resolution [6], framerate [7], and bit-depth raises the need to develop a large-scale, highly efficient video encoding environment [8]. This is crucial for DASH-based content provisioning as it requires encoding multiple representations of the same video content in an encoding server.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low latency Encoding

Menon,

Zhu,

Rajendran

et al. 2024

Proceedings of the 3rd Mile-High Video Conference on ZZZ

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In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70 %, a reduction in the total number of CPU threads used by 63.83 %, and a 37.87 % reduction in the overall encoding time, considering a JND of six VMAF points.

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Section: Video Complexity Feature Extractionmentioning

confidence: 99%