Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression

TL;DR

This paper introduces a novel rate-distortion optimization method for UGC compression that uses non-reference metrics, enabling efficient block-wise bit allocation and achieving over 30% bitrate savings without extra decoder complexity.

Contribution

It proposes a linearized NRM-based RDO approach that simplifies computation and improves compression efficiency for noisy user-generated videos.

Findings

Achieves over 30% bitrate savings compared to SSE-RDO.

No additional decoder complexity introduced.

Minimal increase in encoder complexity.

Abstract

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.