SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time
This work focuses on low bitrate video streaming scenarios (e.g. 50 - 200Kbps) where the video quality is severely compromised. We present a family of novel deep generative models for enhancing perceptual video quality of such streams by performing super-resolution while also removing compression artifacts. Our model, which we call SuperTran, consumes as input a single high-quality, high-resolution reference images in addition to the low-quality, low-resolution video stream. The model thus learns how to borrow or copy visual elements like textures from the reference image and fill in the remaining details from the low resolution stream in order to produce perceptually enhanced output video. The reference frame can be sent once at the start of the video session or be retrieved from a gallery. Importantly, the resulting output has substantially better detail than what has been otherwise possible with methods that only use a low resolution input such as the SuperVEGAN method. SuperTran works in real-time (up to 30 frames/sec) on the cloud alongside standard pipelines.
Code (0)
등록된 구현이 없습니다.
Tasks
Super-ResolutionSimilar Papers 제목 키워드 기반
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
The objective of non-reference video quality assessment is to evaluate the quality of distorted video without access to reference high-definition references. In this study, we introduce an enhanced spatial perception mod…
Image Quality AssessmentVideo Quality AssessmentVisual Question Answering (VQA)Dynamic Transformer for Efficient Machine Translation on Embedded Devices
The Transformer architecture is widely used for machine translation tasks. However, its resource-intensive nature makes it challenging to implement on constrained embedded devices, particularly where available hardware r…
CPUGPUMachine TranslationTranslationGemino: Practical and Robust Neural Compression for Video Conferencing
Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have bee…
GPUSuper-ResolutionReGenVC: End-to-End Real-Time Generative Video Coding at Ultra-Low Bitrate
We present ReGenVC, an end-to-end generative video codec that compresses talking-head video to an ultra-low bitrate and decodes it in real time. The encoder reduces a source clip to a compact bitstream -- a neurally comp…
Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videos
Existing deep facial animation coding techniques efficiently compress talking head videos by applying deep generative models. Instead of compressing the entire video sequence, these methods focus on compressing only the …
Video Reconstruction