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Zhan Ma

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8 papers

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AAAI Conference 2026 Conference Paper

Reinforced Rate Control for Neural Video Compression via Inter-Frame Rate–Distortion Awareness

Wuyang Cong
Junqi Shi
Lizhong Wang
Weijing Shi
Ming Lu
Hao Chen
Zhan Ma

Neural video compression (NVC) has demonstrated superior compression efficiency, yet effective rate control remains a significant challenge due to complex temporal dependencies. Existing rate control schemes typically leverage frame content to capture distortion interactions, overlooking inter-frame rate dependencies arising from shifts in per-frame coding parameters. This often leads to suboptimal bitrate allocation and cascading parameter decisions. To address this, we propose a reinforcement‑learning (RL)‑based rate control framework that formulates the task as a frame‑by‑frame sequential decision process. At each frame, an RL agent observes a spatiotemporal state and selects coding parameters to optimize a long‑term reward that reflects rate‑distortion (R-D) performance and bitrate adherence. Unlike prior methods, our approach jointly determines bitrate allocation and coding configuration in a single step, independent of group‑of‑pictures (GOP) structure. Extensive experiments across diverse NVC architectures show that our method reduces the average relative bitrate error to 1.20 percent and achieves up to 13.45 percent bitrate savings at typical GOP sizes, outperforming existing approaches. In addition, our framework demonstrates improved robustness to content variation and bandwidth fluctuations with lower encoding/decoding overhead, making it highly suitable for practical deployment.

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NeurIPS Conference 2025 Conference Paper

Neural B-frame Video Compression with Bi-directional Reference Harmonization

Yuxi Liu
jin dengchao
Shuai Huo
Jiawen Gu
Chao Zhou
Huihui Bai
Ming Lu
Zhan Ma

Neural video compression (NVC) has made significant progress in recent years, while neural B-frame video compression (NBVC) remains underexplored compared to P-frame compression. NBVC can adopt bi-directional reference frames for better compression performance. However, NBVC's hierarchical coding may complicate continuous temporal prediction, especially at some hierarchical levels with a large frame span, which could cause the contribution of the two reference frames to be unbalanced. To optimize reference information utilization, we propose a novel NBVC method, termed Bi-directional Reference Harmonization Video Compression (BRHVC), with the proposed Bi-directional Motion Converge (BMC) and Bi-directional Contextual Fusion (BCF). BMC converges multiple optical flows in motion compression, leading to more accurate motion compensation on a larger scale. Then BCF explicitly models the weights of reference contexts under the guidance of motion compensation accuracy. With more efficient motions and contexts, BRHVC can effectively harmonize bi-directional references. Experimental results indicate that our BRHVC outperforms previous state-of-the-art NVC methods, even surpassing the traditional coding, VTM-RA (under random access configuration), on the HEVC datasets. The source code will be released. The source code is released at https: //github. com/kwai/NVC.

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AAAI Conference 2025 Conference Paper

Towards Loss-Resilient Image Coding for Unstable Satellite Networks

Hongwei Sha
Muchen Dong
Quanyou Luo
Ming Lu
Hao Chen
Zhan Ma

Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. To address it, we propose a loss-resilient image coding approach that leverages end-to-end optimization in learned image compression (LIC). Our method builds on the channel-wise progressive coding framework, incorporating Spatial-Channel Rearrangement (SCR) on the encoder side and Mask Conditional Aggregation (MCA) on the decoder side to improve reconstruction quality with unpredictable errors. By integrating the Gilbert-Elliot model into the training process, we enhance the model's ability to generalize in real-world network conditions. Extensive evaluations show that our approach outperforms traditional and deep learning-based methods in terms of compression performance and stability under diverse packet loss, offering robust and efficient progressive transmission even in challenging environments.

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NeurIPS Conference 2024 Conference Paper

All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation

Xu Zhang
Peiyao Guo
Ming Lu
Zhan Ma

Image coding for multi-task applications, catering to both human perception and machine vision, has been extensively investigated. Existing methods often rely on multiple task-specific encoder-decoder pairs, leading to high overhead of parameter and bitrate usage, or face challenges in multi-objective optimization under a unified representation, failing to achieve both performance and efficiency. To this end, we propose Multi-Path Aggregation (MPA) integrated into existing coding models for joint human-machine vision, unifying the feature representation with an all-in-one architecture. MPA employs a predictor to allocate latent features among task-specific paths based on feature importance varied across tasks, maximizing the utility of shared features while preserving task-specific features for subsequent refinement. Leveraging feature correlations, we develop a two-stage optimization strategy to alleviate multi-task performance degradation. Upon the reuse of shared features, as low as 1. 89\% parameters are further augmented and fine-tuned for a specific task, which completely avoids extensive optimization of the entire model. Experimental results show that MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization across human viewing and machine analysis tasks. Moreover, our all-in-one design supports seamless transitions between human- and machine-oriented reconstruction, enabling task-controllable interpretation without altering the unified model. Code is available at https: //github. com/NJUVISION/MPA.

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AAAI Conference 2024 Conference Paper

Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding

Yichi Zhang
Zhihao Duan
Ming Lu
Dandan Ding
Fengqing Zhu
Zhan Ma

While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image. As seen, CLIC expands the receptive field into the entire image for intra-cluster feature aggregation. Afterward, features are reordered to their original spatial positions to pass through the local attention units for inter-cluster embedding. Additionally, we introduce the Guided Post-Quantization Filtering (GuidedPQF) into CLIC, effectively mitigating the propagation and accumulation of quantization errors at the initial decoding stage. Extensive experiments demonstrate the superior performance of CLIC over state-of-the-art works: when optimized using MSE, it outperforms VVC by about 10% BD-Rate in three widely-used benchmark datasets; when optimized using MS-SSIM, it saves more than 50% BD-Rate over VVC. Our CLIC offers a new way to generate compact representations for image compression, which also provides a novel direction along the line of LIC development.

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AAAI Conference 2024 Conference Paper

Deep Hierarchical Video Compression

Ming Lu
Zhihao Duan
Fengqing Zhu
Zhan Ma

Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results. Existing methods using a single-scale Variational AutoEncoder (VAE) must devise complex networks for conditional probability estimation in latent space, neglecting multiscale characteristics of video frames. Instead, this work proposes hierarchical probabilistic predictive coding, for which hierarchal VAEs are carefully designed to characterize multiscale latent features as a family of flexible priors and posteriors to predict the probabilities of future frames. Under such a hierarchical structure, lightweight networks are sufficient for prediction. The proposed method outperforms representative learned video compression models on common testing videos and demonstrates computational friendliness with much less memory footprint and faster encoding/decoding. Extensive experiments on adaptation to temporal patterns also indicate the better generalization of our hierarchical predictive mechanism. Furthermore, our solution is the first to enable progressive decoding that is favored in networked video applications with packet loss.

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IJCAI Conference 2024 Conference Paper

Encoding Auxiliary Information to Restore Compressed Point Cloud Geometry

Gexin Liu
Jiahao Zhu
Dandan Ding
Zhan Ma

The standardized Geometry-based Point Cloud Compression (G-PCC) suffers from limited coding performance and low-quality reconstruction. To address this, we propose AuxGR, a performance-complexity tradeoff solution for point cloud geometry restoration: leveraging auxiliary bitstream to enhance the quality of G-PCC compressed point cloud geometry. This auxiliary bitstream efficiently encapsulates spatio-temporal information. For static coding, we perform paired information embedding (PIE) on the G-PCC decoded frame by employing target convolutions from its original counterpart, producing an auxiliary bitstream containing abundant original information. For dynamic coding, in addition to PIE, we propose temporal information embedding (TIE) to capture motion information between the previously restored and the current G-PCC decoded frames. TIE applies target kNN attention between them, which ensures the temporal neighborhood construction for each point and implicitly represents motions. Due to the similarity across temporal frames, only the residuals between TIE and PIE outputs are compressed as auxiliary bitstream. Experimental results demonstrate that AuxGR notably outperforms existing methods in both static and dynamic coding scenarios. Moreover, our framework enables the flexible incorporation of auxiliary information under computation constraints, which is attractive to real applications.

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AAAI Conference 2020 Conference Paper

Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

Haojie Liu
Han Shen
Lichao Huang
Ming Lu
Tong Chen
Zhan Ma

Traditional video compression technologies have been developed over decades in pursuit of higher coding efﬁciency. Ef- ﬁcient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both ﬁrst-order optical ﬂow and second-order ﬂow prediction. We suggest an one-stage learning approach to encapsulate ﬂow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efﬁciency Video Coding (H. 265/HEVC), H. 264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-theart performance, with consistent gains across all popular test sequences.

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