Junqi Liao

University of Science and Technology of China

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Co-Authors

Dong Liu

University of Science and Technology of China

Houqiang Li

University of Science and Technology of China

Li Li

University of Science and Technology of China

Xihua Sheng

University of Science and Technology of China

Haotian Zhang

University of Science and Technology of China

Yuqi Li

Zhejiang University of Technology

Yiheng Jiang

University of Science and Technology of China

Yifan Bian

University of Science and Technology of China

Xiaomin Song

Materials Science & Engineering

Chuanbo Tang

University of Science and Technology of China

Cooperative Institutions

University of Science and Technology of China

Chinese Academy of Sciences

Northeastern University

Wuhan University

Beijing Institute of Technology

Zhejiang University

Dalian Medical University

Huazhong University of Science and Technology

Beijing University of Posts and Telecommunications

Tianjin University

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Padding-Aware Learned Image Compression

2022 IEEE International Symposium on Circuits and Systems (ISCAS) (2023)

Haotian Zhang Junqi Liao Yiheng Jiang Li Li Dong Liu

For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previous studies ignore padded images in the training process. In this paper, we analyze the impact of padding on compression performance. Then, we propose a padding-aware training (PAT) strategy, handling the padding effect during the training. Specifically, our PAT strategy calculates the loss of pre-padding image through a masking operation. Finally, according to our systematic experimental results, we find that images with different resolutions tend to favor different padding modes. Therefore, we further propose to conduct padding mode decision in the encoding process for rate-distortion optimization. Experiments demonstrate that our proposed PAT strategy and padding mode decision effectively compensate for the performance drop caused by padding.

Padding

Distortion (music)

10.1109/iscas46773.2023.10181476

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USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s

arXiv (Cornell University) (2024)

Zhuoyuan Li Junqi Liao Chuanbo Tang Haotian Zhang Yuqi Li

Image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-related research, practical applications, and standardization activities. We put forward a test dataset namely USTC-TD, which has been successfully adopted in the practical end-to-end image/video coding challenge of the IEEE International Conference on Visual Communications and Image Processing in 2022 and 2023. USTC-TD contains 40 images at 4K spatial resolution and 10 video sequences at 1080p spatial resolution, featuring various content due to the diverse environmental factors (scene type, texture, motion, view) and the designed imaging factors (illumination, shadow, lens). We quantitatively evaluate USTC-TD on different image/video features (spatial, temporal, color, lightness), and compare it with the previous image/video test datasets, which verifies the wider coverage and more diversity of the proposed dataset. We also evaluate both classic standardized and recent learned image/video coding schemes on USTC-TD with PSNR and MS-SSIM, and provide an extensive benchmark for the evaluated schemes. Based on the characteristics and specific design of the proposed test dataset, we analyze the benchmark performance and shed light on the future research and development of image/video coding. All the data are released online: https://esakak.github.io/USTC-TD.

Benchmark (surveying)

10.48550/arxiv.2409.08481

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Content-adaptive Rate-Distortion Modeling for Frame-level Rate Control in Versatile Video Coding

IEEE Transactions on Multimedia (2024)

Junqi Liao Li Li Dong Liu Houqiang Li

Rate control (RC) plays an essential role in video coding. RC algorithms based on more accurate rate-distortion (R-D) models often achieve higher control precision and better R-D performance. The most widely used R-D model for HEVC and VVC is the hyperbolic R-D model, which is equivalent to a linear relationship between

$\ln {R}$

and

$\ln {D}$

. Due to its high accuracy, few studies have attempted to improve the accuracy of the hyperbolic R-D model further. Intuitively, we consider that the accuracy of the hyperbolic R-D model could be further improved by increasing the order of the R-D model. However, this may also increase the number of model parameters to be estimated, which may not benefit the one-pass RC precision. In this paper, we first explicitly note that there is a trade-off between the order of the R-D model and the difficulty in estimating the model parameters in one-pass RC. Then, motivated by the trade-off, we propose high-order R-D models and the corresponding one-pass frame-level RC algorithms for video coding. Finally, we introduce the quadratic R-D model into frame-level RC in the VVC Test Model (VTM-19.2) and provide a content-adaptive model selection between the first-order and second-order R-D models. Experimental results show that the proposed frame-level RC algorithm based on the quadratic R-D model reduces the average frame-level bitrate error by 5.30%, 3.11%, and 13.45% and achieves 0.49%, 0.65%, and 0.35% BD-Rate savings under Low Delay B (LDB), Low Delay P (LDP), and Random Access (RA) configurations, respectively, when compared to the default RC algorithm used in VTM-19.2.

Rate distortion

Rate–distortion theory

10.1109/tmm.2024.3358063

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Frame Level Content Adaptive λ for Neural Video Compression

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) (2024)

Zhongyuan Zuo Junqi Liao Xiaomin Song Zheng Liu Huiming Zheng

Content (measure theory)

10.1109/vcip63160.2024.10849937

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Reinforcement Learning-based Frame-level Bit Allocation for VVC

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) (2023)

Junqi Liao Li Li Dong Liu Houqiang Li

As frame-level bit allocation is a dependent sequential decision-making problem, it can be modeled as a Markov Decision Process (MDP) and solved by reinforcement learning (RL). Existing reports using RL for coding mainly have two problems: unrepresentative handcrafted features and limited interaction efficiency. In this paper, to address these two problems, we first propose to use a deep neural network to extract features from the state, which is designed as a combination of raw pixel-level information and handcrafted features. The network is able to extract information necessary for the following decision-making neural network automatically. We then propose an efficient training scheme. By designing a parallel interaction algorithm and using a simplified video encoder, our proposed scheme can train on a sophisticated encoder such as VTM. To the best of our knowledge, this is the first RL algorithm implemented for bit allocation in VTM. The experimental results show that our scheme can learn interpretable frame-level bit allocation and achieves a better rate-distortion (R-D) performance compared with VTM.

Bit (key)

10.1109/vcip59821.2023.10402665

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Practical Learned Image Compression with Online Encoder Optimization

Haotian Zhang Feihong Mei Junqi Liao Li Li Houqiang Li

Learned image compression methods have shown significant advances in performance. However, they often suffer from higher decoding complexity compared to traditional codecs. In this paper, we present an approach toward practical learned image compression that focuses on faster decoding by employing online encoder optimization. To reduce network complexity, we employ a hyperprior structure with smaller convolution kernels, which enables efficient compression. We also introduce a content adaptive skipping algorithm to accelerate entropy decoding. By jointly optimizing entropy decoding complexity and the distortion of the reconstructed image at the encoder side, we achieve faster decoding while maintaining the quality of the reconstructed image. Furthermore, we incorporate online iterative optimization at the encoder side to enhance rate-distortion performance without increasing decoding complexity. This approach allows us to fine-tune the latent and improve overall performance. Our method achieves a notable improvement over BPG on the VCIP 2023 challenge test set, with more than 1 dB increase in PSNR and a 50% reduction in decoding time.

10.1109/pcs60826.2024.10566341

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