Rate Model for Compressed Video Considering Impacts Of Spatial, Temporal and Amplitude Resolutions and Its Applications for Video Coding and Adaptation

TL;DR

This paper introduces an analytical rate model for compressed video that accounts for spatial, temporal, and amplitude resolutions, enabling better rate control and adaptation in video coding.

Contribution

The paper proposes a content-dependent, power-function-based rate model for compressed video considering STAR, with methods to predict model parameters from original video features.

Findings

Model fits measured data with high Pearson correlation (PC > 0.99).

Applicable across various coding scenarios with high accuracy.

Enables optimization of STAR for given rate constraints.

Abstract

In this paper, we investigate the impacts of spatial, temporal and amplitude resolution (STAR) on the bit rate of a compressed video. We propose an analytical rate model in terms of the quantization stepsize, frame size and frame rate. Experimental results reveal that the increase of the video rate as the individual resolution increases follows a power function. Hence, the proposed model expresses the rate as the product of power functions of the quantization stepsize, frame size and frame rate, respectively. The proposed rate model is analytically tractable, requiring only four content dependent parameters. We also propose methods for predicting the model parameters from content features that can be computed from original video. Simulation results show that model predicted rates fit the measured data very well with high Pearson correlation (PC) and small relative root mean square error (RRMSE). The same model function works for different coding scenarios (including scalable and non-scalable video, temporal prediction using either hierarchical B or IPPP structure, etc.) with very high accuracy (average PC $>$ 0.99), but the values of model parameters differ. Using the proposed rate model and the quality model introduced in a separate work, we show how to optimize the STAR for a given rate constraint, which is important for both encoder rate control and scalable video adaptation. Furthermore, we demonstrate how to order the spatial, temporal and amplitude layers of a scalable video in a rate-quality optimized way.