A Parametric Rate-Distortion Model for Video Transcoding
Over the past two decades, the surge in video streaming applications has been fueled by the increasing accessibility of the internet and the growing demand for network video. As users with varying internet speeds and devices seek high-quality video, transcoding becomes essential for service providers. In this paper, we introduce a parametric rate-distortion (R-D) transcoding model. Our model excels at predicting transcoding distortion at various rates without the need for encoding the video. This model serves as a versatile tool that can be used to achieve visual quality improvement (in terms of PSNR) via trans-sizing. Moreover, we use our model to identify visually lossless and near-zero-slope bitrate ranges for an ingest video. Having this information allows us to adjust the transcoding target bitrate while introducing visually negligible quality degradations. By utilizing our model in this manner, quality improvements up to 2 dB and bitrate savings of up to 46% of the original target bitrate are possible. Experimental results demonstrate the efficacy of our model in video transcoding rate distortion prediction.
Code (0)
등록된 구현이 없습니다.
Tasks
modelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
High-Quality Live Video Streaming via Transcoding Time Prediction and Preset Selection
Video streaming often requires transcoding content into different resolutions and bitrates to match the recipient's internet speed and screen capabilities. Video encoders like x264 offer various presets, each with differ…
PredictionFull-reference Video Quality Assessment for User Generated Content Transcoding
Unlike video coding for professional content, the delivery pipeline of User Generated Content (UGC) involves transcoding where unpristine reference content needs to be compressed repeatedly. In this work, we observe that…
Video Quality AssessmentVisual Question Answering (VQA)BVI-UGC: A Video Quality Database for User-Generated Content Transcoding
In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its visual quality through subjective tests…
Video Quality AssessmentFilling the gaps in video transcoder deployment in the cloud
Cloud-based deployment of content production and broadcast workflows has continued to disrupt the industry after the pandemic. The key tools required for unlocking cloud workflows, e.g., transcoding, metadata parsing, an…
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) mode…
Deep Reinforcement Learning