An algorithm for a quality-optimized bit rate ladder generation for video streaming services using a neural network

Andreas Kah; Maurice Klein; Christoph Burgmair; Markus Rasokat; Wolfgang Ruppel; Matthias Narroschke

doi:10.1117/12.2631923

3 October 2022 An algorithm for a quality-optimized bit rate ladder generation for video streaming services using a neural network

Andreas Kah, Maurice Klein, Christoph Burgmair, Markus Rasokat, Wolfgang Ruppel, Matthias Narroschke

Author Affiliations +

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 1222611 (2022) https://doi.org/10.1117/12.2631923
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

For video streaming services, a bit rate ladder is generated by encoding each video signal at various bit rates and associated spatial resolutions. For a bit rate ladder that maximizes the subjective quality at a minimum bit rate, it was found that the VMAF of the highest provided quality should not exceed 95, which is on average associated with the same subjective quality as the original signal. Second, all VMAF differences between adjacent renditions should ideally be not greater than 2 as this guarantees indistinguishable subjective quality on average. The generation of a bit rate ladder fulfilling these constraints faces the difficulties that (i) today’s encoders cannot be instructed to achieve a certain VMAF and (ii) a certain VMAF can be achieved by various combinations of bit rate and spatial resolution. These difficulties result in a content-dependent multidimensional solution space for generating the quality-based bit rate ladder at a minimum bit rate. In this paper, an algorithm is presented which can generate such a bit rate ladder. The algorithm determines the VMAF of nine initial encodings of the signal. Using a specifically designed and trained neural network, the VMAF of 5805 combinations of bit rate and spatial resolution is predicted from the nine initial ones. Based on these predictions, a bit rate ladder is extracted and further refined until all VMAF constraints are fulfilled. Experiments show that the algorithm requires 3.6 encodings per provided VMAF on average. A VMAF of 95.07 is achieved on average for the highest provided quality and a VMAF difference between adjacent renditions of 1.92.

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Andreas Kah, Maurice Klein, Christoph Burgmair, Markus Rasokat, Wolfgang Ruppel, and Matthias Narroschke "An algorithm for a quality-optimized bit rate ladder generation for video streaming services using a neural network", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 1222611 (3 October 2022); https://doi.org/10.1117/12.2631923

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