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AOM CTC Class A1 Dataset
The AOM CTC Class A1 dataset is a high-resolution video dataset used for evaluating the performance of video compression algorithms. -
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
A video compression degradation model to synthesize low-resolution image data in the blind SISR task. -
HEVC Test Sequences
The dataset used for training and testing the proposed DNN-based frame prediction architecture. -
Deep Frame Prediction for Video Coding
The proposed DNN-based frame prediction architecture that is able to support both uni- and bi-directional prediction. -
Ultra Video Group test sequences
The dataset used in the paper is the UVG dataset, which contains 7 1080p video sequences and a total of 3900 frames. -
Learning for video compression with hierarchical quality and recurrent enhanc...
A learning-based video compression method -
Learning image and video compression through spatial-temporal energy compaction
A learning-based video compression method -
FVC: A New Framework towards Deep Video Compression in Feature Space
A video compression framework that performs all operations in the feature space -
JCT-VC Dataset
A dataset of images and videos for video compression research. -
JVET Common Test Conditions
The dataset used in the paper is a collection of video sequences with varying resolutions and bit depths, used for training and testing the proposed video compression framework. -
ViSTRA2 Dataset
The dataset used in the paper is a collection of video sequences with varying resolutions and bit depths, used for training and testing the proposed video compression framework. -
360o Video Dataset
The dataset used in the paper is a 360o video dataset, which is used to evaluate the performance of the proposed distortion-aware loop filtering model. -
Low-Rank-Based Nonlocal Adaptive Loop Filter for High-Efficiency Video Compre...
The proposed non-local adaptive loop filter for HEVC. -
Residual Highway Convolutional Neural Networks for In-Loop Filtering in HEVC
The proposed RHCNN model for in-loop filtering in HEVC. -
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
The proposed MIF approach enhances the quality of each encoded frame leveraging multiple adjacent frames.
