51 papers with code • 0 benchmarks • 3 datasets
Video Compression is a process of reducing the size of an image or video file by exploiting spatial and temporal redundancies within an image or video frame and across multiple video frames. The ultimate goal of a successful Video Compression system is to reduce data volume while retaining the perceptual quality of the decompressed data.
These leaderboards are used to track progress in Video Compression
LibrariesUse these libraries to find Video Compression models and implementations
Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.
At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes.
Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression.
In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure
In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS).
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting.
The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.