Rate-Distortion Optimization With Alternative References For UGC Video Compression
User generated content (UGC) refers to videos that are uploaded by users and shared over the Internet. UGC may have low quality due to noise and previous compression. When re-encoding UGC for streaming or downloading, a traditional video coding pipeline will perform rate-distortion (RD) optimization to choose coding parameters. However, in the UGC video coding case, since the input is not pristine, quality ``saturation'' (or even degradation) can be observed, i.e., increased bitrate only leads to improved representation of coding artifacts and noise present in the UGC input. In this paper, we study the saturation problem in UGC compression, where the goal is to identify and avoid during encoding, the coding parameters and rates that lead to quality saturation. We proposed a geometric criterion for saturation detection that works with rate-distortion optimization, and only requires a few frames from the UGC video. In addition, we show how to combine the proposed saturation detection method with existing video coding systems that implement rate-distortion optimization for efficient compression of UGC videos.
Code (0)
등록된 구현이 없습니다.
Tasks
Video CompressionSimilar Papers 제목 키워드 기반
Best of Both Distortion Worlds
We study the problem of designing voting rules that take as input the ordinal preferences of $n$ agents over a set of $m$ alternatives and output a single alternative, aiming to optimize the overall happiness of the agen…
Randomized Social Choice Functions Under Metric Preferences
We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric. We assume that these c…
Thinking with Frames: Generative Video Distortion Evaluation via Frame Reward Model
Recent advances in video reward models and post-training strategies have improved text-to-video (T2V) generation. While these models typically assess visual quality, motion quality, and text alignment, they often overloo…
Reinforcement LearningAugmented Deep Contexts for Spatially Embedded Video Coding
Most Neural Video Codecs (NVCs) only employ temporal references to generate temporal-only contexts and latent prior. These temporal-only NVCs fail to handle large motions or emerging objects due to limited contexts and m…
Aligning Anime Video Generation with Human Feedback
Anime video generation faces significant challenges due to the scarcity of anime data and unusual motion patterns, leading to issues such as motion distortion and flickering artifacts, which result in misalignment with h…
Video Generation