Modeling Rate and Perceptual Quality of Scalable Video as Functions of Quantization and Frame Rate and Its Application in Scalable Video Adaptation

This paper investigates the impact of frame rate and quantization on the bit rate and perceptual quality of a scalable video with temporal and quality scalability. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame-rate. The quality model is derived from our earlier quality model in terms of the PSNR of decoded frames and frame rate. Both models are developed based on the key observation from experimental data that the relative reduction of either rate and quality when the frame rate decreases is quite independent of the quantization stepsize. This observation enables us to express both rate and quality as the product of separate functions of quantization stepsize and frame rate, respectively. The proposed rate and quality models are analytically tractable, each requiring only two content-dependent parameters. Both models fit the measured data very accurately, with high Pearson correlation. We further apply these models for rate-constrained bitstream adaptation, where the problem is to determine the optimal combination of quality and temporal layers that provides the highest perceptual quality for a given bandwidth constraint.