用无参考指标优化短视频压缩，效率提升30%以上。
Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression
- 将无参考质量评估线性化，实现块级比特分配
- 在图像视频上实现超30%码率节省，无解码开销
- 适合处理低质量用户生成内容的压缩场景
在线视频平台需编码大量噪声干扰的用户生成内容（UGC）。传统基于全参考质量指标（FRMs）的率失真优化（RDO）在处理低质输入时会过度保留伪影，导致压缩效率下降。而无参考指标（NRMs）更适合评估UGC质量，但使用NRMs进行RDO需反复编码、解码与评估，计算成本过高。本文通过在未压缩视频附近线性化NRM，构建可直接用于变换域块级比特分配的代价函数，通过量化误差与NRM梯度对齐来估计失真。为防止偏离输入过远，引入平方误差和（SSE）正则项，并推导出与SSE-RDO类似的拉格朗日系数和正则化参数表达式。实验表明，在图像与视频上相比SSE-RDO，该方法在保持目标NRM的前提下实现超过30%的码率节省，且解码复杂度不变，编码复杂度仅小幅增加。
原文摘要 · Abstract (English)
Service providers must encode a large volume of noisy videos to meet the demand for user-generated content (UGC) in online video-sharing platforms. However, low-quality UGC challenges conventional codecs based on rate-distortion optimization (RDO) with full-reference metrics (FRMs). While effective for pristine videos, FRMs drive codecs to preserve artifacts when the input is degraded, resulting in suboptimal compression. A more suitable approach used to assess UGC quality is based on non-reference metrics (NRMs). However, RDO with NRMs as a measure of distortion requires an iterative workflow of encoding, decoding, and metric evaluation, which is computationally impractical. This paper overcomes this limitation by linearizing the NRM around the uncompressed video. The resulting cost function enables block-wise bit allocation in the transform domain by estimating the alignment of the quantization error with the gradient of the NRM. To avoid large deviations from the input, we add sum of squared errors (SSE) regularization. We derive expressions for both the SSE regularization parameter and the Lagrangian, akin to the relationship used for SSE-RDO. Experiments with images and videos show bitrate savings of more than 30\% over SSE-RDO using the target NRM, with no decoder complexity overhead and minimal encoder complexity increase.
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