Prediction and Reference Quality Adaptation for Learned Video Compression
Temporal prediction is one of the most important technologies for video compression. Various prediction coding modes are designed in traditional video codecs. Traditional video codecs will adaptively to decide the optimal coding mode according to the prediction quality and reference quality. Recently, learned video codecs have made great progress. However, they did not effectively address the problem of prediction and reference quality adaptation, which limits the effective utilization of temporal prediction and reduction of reconstruction error propagation. Therefore, in this paper, we first propose a confidence-based prediction quality adaptation (PQA) module to provide explicit discrimination for the spatial and channel-wise prediction quality difference. With this module, the prediction with low quality will be suppressed and that with high quality will be enhanced. The codec can adaptively decide which spatial or channel location of predictions to use. Then, we further propose a reference quality adaptation (RQA) module and an associated repeat-long training strategy to provide dynamic spatially variant filters for diverse reference qualities. With these filters, our codec can adapt to different reference qualities, making it easier to achieve the target reconstruction quality and reduce the reconstruction error propagation. Experimental results verify that our proposed modules can effectively help our codec achieve a higher compression performance.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionVideo CompressionSimilar Papers 제목 키워드 기반
Deep Priors for Video Quality Prediction
In this work, we designed a completely blind video quality assessment algorithm using the deep video prior. This work mainly explores the utility of deep video prior in estimating the visual quality of the video. In our …
PredictionVideo Quality AssessmentUnsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
During the last years, convolutional neural networks (CNNs) have triumphed over video quality assessment (VQA) tasks. However, CNN-based approaches heavily rely on annotated data which are typically not available in …
Domain AdaptationUnsupervised Domain AdaptationVideo Quality AssessmentVisual Question Answering (VQA)Content Adaptive based Motion Alignment Framework for Learned Video Compression
Recent advances in end-to-end video compression have shown promising results owing to their unified end-to-end learning optimization. However, such generalized frameworks often lack content-specific adaptation, leading t…
Versatile Learned Video Compression
Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned video compression schemes are limited b…
Motion CompensationMS-SSIMPredictionSSIM+1Video Quality Assessment for Computer Graphics Applications
Numerous current Computer Graphics methods produce video sequences as their outcome. The merit of these methods is often judged by assessing the quality of a set of results through lengthy user studies. We present a full…
Tone MappingVideo CompressionVideo Quality Assessment