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

arXiv: https://arxiv.org/abs/2208.10739 · HTML: https://mlsanity.com/paper/2208.10739/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming](https://mlsanity.com/paper/2102.04550/) (2021)

## Built on by (most cited first, 2 of 2)

- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications](https://mlsanity.com/paper/2510.12379/) (2025)

## Abstract (arXiv)

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. Video encoder uses the RF to compress segment as the first encoding pass. Then VMAF quality of the first pass encoding is measured. If the quality doesn't meet target, a second pass RF prediction and encoding will be performed. With the help of first pass predicted RF and corresponding actual quality as feedback, the second pass prediction will be highly accurate. Experiments show the proposed method requires only 1.55 times encoding complexity on average, meanwhile the accuracy, that the compressed video's actual VMAF is within ±1 around the target VMAF, reaches 98.88%.