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Video Compression

7 papers with code · Computer Vision
Subtask of Video

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Greatest papers with code

Semantic Perceptual Image Compression using Deep Convolution Networks

27 Dec 2016iamaaditya/image-compression-cnn

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

IMAGE COMPRESSION OBJECT DETECTION VIDEO COMPRESSION

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

CVPR 2019 GuoLusjtu/DVC

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

OPTICAL FLOW ESTIMATION VIDEO COMPRESSION

Disentangled Sequential Autoencoder

ICML 2018 yatindandi/Disentangled-Sequential-Autoencoder

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

VIDEO COMPRESSION

MGANet: A Robust Model for Quality Enhancement of Compressed Video

22 Nov 2018mengab/MGANet

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.

VIDEO COMPRESSION

DeepCache: Principled Cache for Mobile Deep Vision

1 Dec 2017xumengwei/DeepCache

We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision.

VIDEO COMPRESSION

Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure

28 Jan 2019mengab/SDTS

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).

VIDEO COMPRESSION

Feature Map Transform Coding for Energy-Efficient CNN Inference

26 May 2019CompressTeam/TransformCodingInference

We analyze the performance of our approach on a variety of CNN architectures and demonstrated FPGA implementation of ResNet18 with our approach results in reduction of around 40% in the memory energy footprint compared to quantized network with negligible impact on accuracy.

VIDEO COMPRESSION