Video Compression
101 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.
Source: Adversarial Video Compression Guided by Soft Edge Detection
Benchmarks
These leaderboards are used to track progress in Video Compression
Libraries
Use these libraries to find Video Compression models and implementationsLatest papers
VQ-NeRV: A Vector Quantized Neural Representation for Videos
This block incorporates a codebook mechanism to discretize the network's shallow residual features and inter-frame residual information effectively.
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets
Third, variable rate quantization is used also for the hyper latent.
Neural Video Compression with Feature Modulation
This results in a better learning of the quantization scaler and helps our NVC support about 11. 4 dB PSNR range.
Extreme Video Compression with Pre-trained Diffusion Models
The results showcase the potential of exploiting the temporal relations in video data using generative models.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures.
Immersive Video Compression using Implicit Neural Representations
In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec.
Coordinate-Aware Modulation for Neural Fields
Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals.
Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN
Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes.
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
In this work, we for the first time, present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task.
OpenDMC: An Open-Source Library and Performance Evaluation for Deep-learning-based Multi-frame Compression
Video streaming has become an essential component of our everyday routines.