NVC-1B: A Large Neural Video Coding Model (2407.19402v1)

Abstract: The emerging large models have achieved notable progress in the fields of natural language processing and computer vision. However, large models for neural video coding are still unexplored. In this paper, we try to explore how to build a large neural video coding model. Based on a small baseline model, we gradually scale up the model sizes of its different coding parts, including the motion encoder-decoder, motion entropy model, contextual encoder-decoder, contextual entropy model, and temporal context mining module, and analyze the influence of model sizes on video compression performance. Then, we explore to use different architectures, including CNN, mixed CNN-Transformer, and Transformer architectures, to implement the neural video coding model and analyze the influence of model architectures on video compression performance. Based on our exploration results, we design the first neural video coding model with more than 1 billion parameters -- NVC-1B. Experimental results show that our proposed large model achieves a significant video compression performance improvement over the small baseline model, and represents the state-of-the-art compression efficiency. We anticipate large models may bring up the video coding technologies to the next level.

• The paper demonstrates that scaling key components in a neural video coding model yields a 25.1% BD-rate reduction over VTM-13.2.
• It compares CNN, mixed CNN-Transformer, and Transformer architectures, revealing that CNNs excel in local video compression tasks.
• The study highlights practical benefits by reducing storage and transmission costs for video content providers while setting the stage for future model optimizations.

A Comprehensive Review of "NVC-1B: A Large Neural Video Coding Model"

The paper "NVC-1B: A Large Neural Video Coding Model" addresses an under-explored domain of neural video coding by presenting a large-scale model exceeding 1 billion parameters, named NVC-1B. This research responds to the emerging need for efficient video compression techniques, necessitated by the massive growth in video data from applications such as streaming services and video conferencing. Traditional codecs like H.264/AVC, H.265/HEVC, and H.266/VVC, while instrumental in enhancing video compression, show limited scalability in performance compared to the rapid increase in video data demands.

Key Contributions

This study explores the scaling of neural video coding models, guided by successes in related fields such as LLMs and Large Vision Models (LVMs), which have demonstrated performance enhancements through increased model sizes. The research explores scaling different components of a baseline neural video coding model: the motion encoder-decoder, motion entropy model, contextual encoder-decoder, contextual entropy model, and temporal context mining module. Furthermore, it investigates the efficacy of incorporating different architectures like CNN, mixed CNN-Transformer, and pure Transformer architectures.

Model Development and Results

The NVC-1B model evolves from a baseline with approximately 21 million parameters (DCVC-SDD), achieving significant strides in video compression efficiency. Experimental results underscore a marked improvement in compression performance compared to both the baseline and several state-of-the-art methods, with a notable average BD-rate reduction of --25.1% when compared against VTM-13.2, on multiple benchmark datasets. Crucially, the paper delineates the importance of model component scaling: substantial benefits are derived from scaling the contextual encoder-decoder, contextual entropy model, and temporal context mining module, highlighting these areas as key drivers for improved efficiency.

Architectural Insights

A pivotal aspect of this paper is the comparative analysis of model architectures. The experiments reveal that while Transformer layers, known for their capacity to handle long-range dependencies, may offer benefits in related domains, CNN architectures demonstrate superior performance in the context of video coding. This finding suggests that the local processing advantages of CNNs are particularly well-suited to the challenges inherent in video compression tasks.

Practical and Theoretical Implications

From a practical perspective, the introduction of NVC-1B presents the potential for substantial data savings, translating into reduced storage and transmission costs for video content providers. This has far-reaching implications for industries reliant on high-volume video transmission. Theoretically, the paper contributes to the understanding of architectural considerations in neural video coding, supporting further exploration into the scalability of video coding networks.

Future Developments

While this paper opens the door to the potential benefits of large-scale neural video coding models, it also sets the stage for future research. This includes exploring lightweight techniques that might bring down the computational costs associated with such large models and investigating more efficient architectures that could be better optimized for both training stability and performance. Furthermore, considering the rapid advancements in LVMs, integrating advancements from neighboring domains could lead to significant breakthroughs in video coding efficiency.

In conclusion, the paper "NVC-1B: A Large Neural Video Coding Model" makes substantial contributions to the field by demonstrating the feasibility and advantages of large-scale models for video coding. This work exemplifies a methodical approach to exploring model scalability, with impactful insights that will inform future studies and applications in neural video coding and beyond.