In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive encoding (CAE) techniques address this by dynamically adjusting encoding parameters based on video content characteristics. However, existing CAE methods are often tightly coupled with specific encoding strategies, leading to inflexibility. In this paper, we propose a model that predicts both RF-quality and RF-bitrate curves, which can be utilized to derive a comprehensive bitrate-quality curve. This approach facilitates flexible adjustments to the encoding strategy without necessitating model retraining. The model leverages codec features, content features, and anchor features to predict the bitrate-quality curve accurately. Additionally, we introduce an anchor suspension method to enhance prediction accuracy. Experiments confirm that the actual quality metric (VMAF) of the compressed video stays within 1 of the target, achieving an accuracy of 99.14%. By incorporating our quality improvement strategy with the rate-quality curve prediction model, we conducted online A/B tests, obtaining both +0.107% improvements in video views and video completions and +0.064% app duration time.

翻译：在流媒体服务中，视频转码是缓解带宽需求的常见做法。然而，传统方法对所有视频采用统一的速率因子（RF）通常会导致显著的低效性。内容自适应编码（CAE）技术通过根据视频内容特征动态调整编码参数解决了这一问题。但现有CAE方法往往与特定编码策略紧密耦合，导致灵活性不足。本文提出一种预测RF-质量和RF-比特率曲线的模型，该模型可用于推导综合的比特率-质量曲线。该方法支持灵活调整编码策略而无需重新训练模型。本模型利用编解码器特征、内容特征和锚点特征来准确预测比特率-质量曲线。此外，我们引入一种锚点悬挂方法以提升预测精度。实验证实，压缩视频的实际质量度量（VMAF）与目标值的偏差在1以内，准确率达到99.14%。通过将我们的质量改进策略与率-质量曲线预测模型相结合，我们进行了在线A/B测试，获得了视频观看量和视频完成率+0.107%以及应用使用时长+0.064%的提升。