Search Results for author: Di Ma

Found 12 papers, 1 papers with code

Perceptually-inspired super-resolution of compressed videos

no code implementations15 Jun 2021 Di Ma, Mariana Afonso, Fan Zhang, David R. Bull

Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency.

Super-Resolution Video Compression

A Subjective Study on Videos at Various Bit Depths

no code implementations18 Mar 2021 Alex Mackin, Di Ma, Fan Zhang, David Bull

Bit depth adaptation, where the bit depth of a video sequence is reduced before transmission and up-sampled during display, can potentially reduce data rates with limited impact on perceptual quality.

CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement

no code implementations18 Nov 2020 Di Ma, Fan Zhang, David R. Bull

We propose a new Generative Adversarial Network for Compressed Video quality Enhancement (CVEGAN).

Video Compression Video Enhancement

Video Compression with CNN-based Post Processing

no code implementations16 Sep 2020 Fan Zhang, Di Ma, Chen Feng, David R. Bull

In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content.

Video Compression

Video compression with low complexity CNN-based spatial resolution adaptation

no code implementations29 Jul 2020 Di Ma, Fan Zhang, David R. Bull

It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling before encoding and super-resolving at the decoder.

Super-Resolution Video Compression

MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering

no code implementations14 Jul 2020 Di Ma, Fan Zhang, David R. Bull

Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure.

Video Compression

BVI-DVC: A Training Database for Deep Video Compression

no code implementations30 Mar 2020 Di Ma, Fan Zhang, David R. Bull

Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches.

Video Compression

Understanding the Political Ideology of Legislators from Social Media Images

no code implementations22 Jul 2019 Nan Xi, Di Ma, Marcus Liou, Zachary C. Steinert-Threlkeld, Jason Anastasopoulos, Jungseock Joo

In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U. S. House and Senate.

Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask

no code implementations9 May 2018 Xi Zhang, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, Gady Agam

We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.

Motion Estimation Optical Flow Estimation

Lecture video indexing using boosted margin maximizing neural networks

no code implementations2 Dec 2017 Di Ma, Xi Zhang, Xu Ouyang, Gady Agam

This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system.

CGMOS: Certainty Guided Minority OverSampling

1 code implementation21 Jul 2016 Xi Zhang, Di Ma, Lin Gan, Shanshan Jiang, Gady Agam

In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.

Classification General Classification

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