Efficient Quality Controller for Video Encoding
IEEE Visual Communications and Image Processing Conference (VCIP 2026)
December 13–16, 2026
Singapore
[PDF]
Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)
Abstract: Traditional video streaming relies on Adaptive Bitrate (ABR) algorithms that encode videos at fixed bitrate-resolution pairs. As a result, a rate controller is essential to ensure that each encoded representation meets its target bitrate. However, perceptually-aware bitrate ladder construction methods aim to encode videos at a fixed visual quality instead of a fixed bitrate, to avoid under- or over-allocating bits for complex and simple content. In this paper, we propose an efficient quality controller that predicts the Quantization Parameter (QP) required to achieve a target VMAF score for each video segment. The framework supports both CPU-only operation for low-complexity environments and GPU-accelerated inference for improved prediction accuracy. By leveraging content features and target quality levels, our model estimates appropriate QP values without requiring pre-encoding or tight integration with the encoder. For target VMAF scores of 94, 88, and 82, the CPU-only model achieves mean absolute errors (MAEs) of 1.05, 1.24, and 1.34, respectively, comparable to the state-of-the-art errors of 1.14, 1.27, and 1.31, while requiring only a fraction of the computational cost. The GPU-based model further reduces the MAEs to 0.50, 0.49, and 0.47, less than half of the state-of-the-art errors.
