Abstract
Spatial scalable video service has surged in the time of multiple screens. Existing bitrate allocation methods are principled by rate-distortion theory and characterized by iterative encoding, which is accurate yet complex. However, the quasi-quantitative description is preferred in practice of broadcasting. In this paper, we propose a task of bitrate estimation for scalable videos concerning the content, aiming at a more efficient model at the cost of precision. First, we exhibit necessity to build a model for Scalable High Efficiency Video Coding (SHVC) and quantitative relation between video content and bitrate using different encoders. Then, a scalable-video dataset is prepared. It covers various types of content to offer diversity for model training. In the end, multi-linear regression is utilized to estimate the bitrate of scalable videos, with spatial and temporal indices as explanatory variables. Our statistical experiments show the model is able to estimate bitrate after trained on the self-built dataset.
Highlights
L IVE streaming and short video have been popular recently
In the Scalable High Efficiency Video Coding (SHVC) rate control algorithm presented by Li et al [10], by establishing the relationship model between Lagrangian multiplier (λ) and distortion of each level, the enhancement layer’s target bitrate is predicted and optimized
SHVC and High Efficiency Video Coding (HEVC) simulcast are believed to be favorable candidates in source coding with respect to spatial-scalable video distribution
Summary
L IVE streaming and short video have been popular recently. surge of video conference caused by the pandemic in 2020 have brought video encoding technology back to research vision. A typical scenario is receiving different broadcasting services indoors, including Ultra High Definition (UHD) for TV and High Definition (HD) for smartphones [1]. Solutions meeting this requirement are created at both source and channel. Rate-Distortion (R-D) theory dominates currently the research on bitrate allocation, which quantizes close-loop control over encoders with performance degradation [11]. The fine control is excessive in most of the practice scenes where qualitative or quasi-quantitative results are enough It is not uncommon for service vendors to assign decoded programs to a specific channel after bitrate is estimated.
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