Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction

Understand

Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate.

• In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality.
• For each one-shot segment in a video, the proposed method firstly extracts spatial, temporal and pre-coding features by an ultra fast pre-process.
• Based on these features, a RF parameter is predicted by a deep neural network.