Video Compression

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.

Source: Adversarial Video Compression Guided by Soft Edge Detection

Libraries

Use these libraries to find Video Compression models and implementations

Most implemented papers

DVC: An End-to-end Deep Video Compression Framework

GuoLusjtu/DVC CVPR 2019

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.

OpenDVC: An Open Source Implementation of the DVC Video Compression Method

RenYang-home/OpenDVC 29 Jun 2020

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.

Semantic Perceptual Image Compression using Deep Convolution Networks

iamaaditya/image-compression-cnn 27 Dec 2016

Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression.

Disentangled Sequential Autoencoder

yatindandi/Disentangled-Sequential-Autoencoder ICML 2018

This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features.

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

RenYang-home/HLVC CVPR 2020

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.

MGANet: A Robust Model for Quality Enhancement of Compressed Video

mengab/MGANet 22 Nov 2018

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

mengab/SDTS 28 Jan 2019

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).

A Unified End-to-End Framework for Efficient Deep Image Compression

liujiaheng/compression 9 Feb 2020

Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.

Convolutional Tensor-Train LSTM for Spatio-temporal Learning

NVlabs/conv-tt-lstm NeurIPS 2020

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.

Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

RenYang-home/RLVC 24 Jun 2020

The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.