Haichuan Ma - IEEE Xplore Author Profile
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Haichuan Ma

Publications
12
Citations
240
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (19)
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Haichuan Ma

Affiliation

Architecture and Technology Innovation Department
Media Innovation Laboratory
Cloud BU, Shenzhen, Huawei, China

Publication Topics

Biography

Haichuan Ma received the B.S. degree in electrical engineering from Xidian University, Xi’an, China, in 2017, and the Ph.D. degree in electrical engineering from the University of Science and Technology of China (USTC), Hefei, China, in 2022. His research interests include image/video coding, signal processing, and machine learning.(Based on document published on 7 August 2023).
Publications
12
Citations
240
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (19)
Author's Published Works

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Learning a Single Convolutional Layer Model for Low Light Image Enhancement

Yuantong Zhang;Baoxin Teng;Daiqin Yang;Zhenzhong Chen;Haichuan Ma;Gang Li;Wenpeng Ding

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 7 | Journal Article |
Cited by: Papers (6)
Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the architecture of the network to the utmost degree. By utilizing the effective structur...Show More

Learning a Single Convolutional Layer Model for Low Light Image Enhancement

Yuantong Zhang;Baoxin Teng;Daiqin Yang;Zhenzhong Chen;Haichuan Ma;Gang Li;Wenpeng Ding

Year: 2024 | Volume: 34, Issue: 7 | Journal Article |

Temporal Wavelet Transform-Based Low-Complexity Perceptual Quality Enhancement of Compressed Video

Cunhui Dong;Haichuan Ma;Zhuoyuan Li;Li Li;Dong Liu

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 5 | Journal Article |
Cited by: Papers (1)
The past few years have witnessed a great success in applying deep learning to enhance the perceptual quality of compressed video. These methods usually perform frame-by-frame quality enhancement, incurring high computational complexity. Low-complexity perceptual quality enhancement is addressed in this paper, motivated by the observation of temporal correlations among video frames. We propose to ...Show More

Temporal Wavelet Transform-Based Low-Complexity Perceptual Quality Enhancement of Compressed Video

Cunhui Dong;Haichuan Ma;Zhuoyuan Li;Li Li;Dong Liu

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

DBVC: An End-to-End 3-D Deep Biomedical Video Coding Framework

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong;Houqiang Li

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 4 | Journal Article |
Cited by: Papers (2)
Biomedical videos require tremendous storage space and transmission bandwidth, so efficient coding methods are urgently required. Existing methods can be roughly divided into motion-based methods and wavelet-based methods. Motion-based methods use motion estimation designed for natural videos and independently optimize prediction, transform, and entropy coding modules. Wavelet-based methods treat ...Show More

DBVC: An End-to-End 3-D Deep Biomedical Video Coding Framework

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong;Houqiang Li

Year: 2024 | Volume: 34, Issue: 4 | Journal Article |

aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong

IEEE Transactions on Medical Imaging
Year: 2023 | Volume: 42, Issue: 3 | Journal Article |
Cited by: Papers (16)
Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency d...Show More

aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong

Year: 2023 | Volume: 42, Issue: 3 | Journal Article |

Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration

Haichuan Ma;Dong Liu;Feng Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2023 | Volume: 45, Issue: 3 | Journal Article |
Cited by: Papers (9)
Wasserstein generative adversarial network (WGAN) has attracted great attention due to its solid mathematical background, i.e., to minimize the Wasserstein distance between the generated distribution and the distribution of interest. In WGAN, the Wasserstein distance is quantitatively evaluated by the discriminator, also known as the critic. The vanilla WGAN trained the critic with the simple Lips...Show More

Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration

Haichuan Ma;Dong Liu;Feng Wu

Year: 2023 | Volume: 45, Issue: 3 | Journal Article |

Wavelet-Based Learned Scalable Video Coding

Cunhui Dong;Haichuan Ma;Dong Liu;John W. Woods

2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
Scalability is an important requirement for video coding when coded videos stream over dynamic-bandwidth networks. The state-of-the-art scalable video coding schemes adopt layer-based methods upon H.265, represented by the SHVC standard. Compared to layer-based schemes, wavelet-based schemes were suspected less efficient for a long while. We try to improve the compression efficiency of wavelet-bas...Show More

Wavelet-Based Learned Scalable Video Coding

Cunhui Dong;Haichuan Ma;Dong Liu;John W. Woods

Year: 2022 | Conference Paper |

End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform

Haichuan Ma;Dong Liu;Ning Yan;Houqiang Li;Feng Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2022 | Volume: 44, Issue: 3 | Journal Article |
Cited by: Papers (109)
Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to op...Show More

End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform

Haichuan Ma;Dong Liu;Ning Yan;Houqiang Li;Feng Wu

Year: 2022 | Volume: 44, Issue: 3 | Journal Article |

iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong

2021 International Conference on Visual Communications and Image Processing (VCIP)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the...Show More

iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform

Dongmei Xue;Haichuan Ma;Li Li;Dong Liu;Zhiwei Xiong

Year: 2021 | Conference Paper |

Improving Compression Artifact Reduction via End-to-End Learning of Side Information

Haichuan Ma;Dong Liu;Feng Wu

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
We propose to improve neural network-based compression artifact reduction by transmitting side information for the neural network. The side information consists of artifact descriptors that are obtained by analyzing the original and compressed images in the encoder. In the decoder, the received descriptors are used as additional input to a well-designed conditional post-processing neural network. ...Show More

Improving Compression Artifact Reduction via End-to-End Learning of Side Information

Haichuan Ma;Dong Liu;Feng Wu

Year: 2020 | Conference Paper |

Compression Artifact Removal with Ensemble Learning of Neural Networks

Yueyu Hu;Haichuan Ma;Dong Liu;Jiaying Liu

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
We propose to improve the reconstruction quality of DLVC intra coding based on an ensemble of deep restoration neural networks. Different ways are proposed to generate diversity models, and based on these models, the behavior of different integration methods for model ensemble is explored. The experimental results show that model ensemble can bring additional performance gains to post-processing o...Show More

Compression Artifact Removal with Ensemble Learning of Neural Networks

Yueyu Hu;Haichuan Ma;Dong Liu;Jiaying Liu

Year: 2020 | Conference Paper |

iWave: CNN-Based Wavelet-Like Transform for Image Compression

Haichuan Ma;Dong Liu;Ruiqin Xiong;Feng Wu

IEEE Transactions on Multimedia
Year: 2020 | Volume: 22, Issue: 7 | Journal Article |
Cited by: Papers (67)
Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000. Traditionally, wavelets were designed from the signal processing theory with certain assumption on the signal, but natural ima...Show More

iWave: CNN-Based Wavelet-Like Transform for Image Compression

Haichuan Ma;Dong Liu;Ruiqin Xiong;Feng Wu

Year: 2020 | Volume: 22, Issue: 7 | Journal Article |

A CNN-Based Image Compression Scheme Compatible with JPEG-2000

Haichuan Ma;Dong Liu;Ruiqin Xiong;Feng Wu

2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (9)
We propose a convolutional neural network (CNN) based image compression scheme that is compatible with JPEG-2000. Specifically, our scheme reuses the existing JPEG-2000 encoders to achieve bitstream, and features two components in addition to JPEG-2000: bitstream re-compression and decoder-side post-processing. First, we propose an advanced arithmetic codec that adopts CNN-based probability estima...Show More

A CNN-Based Image Compression Scheme Compatible with JPEG-2000

Haichuan Ma;Dong Liu;Ruiqin Xiong;Feng Wu

Year: 2019 | Conference Paper |

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