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Providing quality-constant streams can simultaneously guarantee userexperience and prevent wasting bit-rate. In this paper, we propose a novel deeplearning based two-pass encoder parameter prediction framework to decide ratefactor (RF), with which encoder can output streams with constant quality. Foreach 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 thesefeatures, a RF parameter is predicted by a deep neural network. Video encoderuses the RF to compress segment as the first encoding pass. Then VMAF qualityof the first pass encoding is measured. If the quality doesn't meet target, asecond pass RF prediction and encoding will be performed. With the help offirst pass predicted RF and corresponding actual quality as feedback, thesecond pass prediction will be highly accurate. Experiments show the proposedmethod requires only 1.55 times encoding complexity on average, meanwhile theaccuracy, that the compressed video's actual VMAF is within $\pm1$ around thetarget VMAF, reaches 98.88%.

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