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Quantitative Analysis of Intra Coding Tools in the Enhanced Compression Model for Next-Generation Video Coding


Core Concepts
This paper provides a quantitative analysis of intra coding tools developed for the Enhanced Compression Model (ECM), which is the reference software for the next-generation video codec being explored by the Joint Video Experts Team (JVET). The analysis focuses on the selection rate of various luma and chroma intra coding tools across different ECM versions, video resolutions, and bitrates, offering insights to the standardization community.
Abstract
The paper presents a quantitative analysis of intra coding tools that are currently being developed and adopted in the Enhanced Compression Model (ECM) for the purpose of the post-VVC exploration at the Joint Video Experts Team (JVET). Key highlights: The analysis focuses on the selection rate of various luma and chroma intra coding tools, including IPM-based, BV-based, and other tools for luma, and IPM-based, cross-component, and other tools for chroma. The selection rate statistics are provided for different ECM versions, video resolutions (JVET CTC classes), and bitrates (quantization parameters). The analysis shows that simpler tools are being replaced by more advanced tools that require more encoder and/or decoder-side processing, such as template-based coding and texture analysis. Data-driven tools, both in the form of offline training (e.g., MIP) and online training (e.g., cross-component models), are becoming more prevalent. The statistical behavior of the tools is generally stable across the JVET CTC and BVI-DVC datasets, indicating that the exploration phase is progressing steadily towards the next-generation codec. The paper concludes by suggesting that a similar quantitative study on inter coding tools of ECM could be conducted as future work.
Stats
The BD-Rate performance of ECM versions over VTM in All-Intra (AI) configurations is provided: ECM-11.0 software provides -12.8%, -23.7%, -24.8% coding gain over VTM in luma, Cb, and Cr components, respectively.
Quotes
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Key Insights Distilled From

by Mohs... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07872.pdf
Video Compression Beyond VVC

Deeper Inquiries

How do the coding tool selection rates and interactions change when considering different block sizes or coding configurations (e.g., random access, low delay)?

In the context of coding tool selection rates and interactions, the change in block sizes or coding configurations can have a significant impact. When considering different block sizes, the selection rates of coding tools may vary based on the granularity of the blocks. Smaller block sizes may lead to more frequent tool selections as the content within each block becomes more diverse, requiring different coding strategies. On the other hand, larger block sizes may result in more stable tool selection rates as the coding decisions are made at a coarser level. In terms of coding configurations like random access or low delay, the interactions between coding tools can also be influenced. For random access configurations, where each frame is coded independently, the selection rates of tools may be more consistent across frames as there is no temporal dependency. Conversely, in low delay configurations where inter-frame dependencies are considered, the tool selection rates may vary based on the content similarity between frames and the efficiency of inter-frame prediction. Overall, changes in block sizes and coding configurations can impact the selection rates and interactions of coding tools by altering the spatial and temporal characteristics of the video content being encoded.

What are the potential implications of the observed trends in tool selection rates on the overall complexity and computational requirements of the next-generation video codec?

The observed trends in tool selection rates can have several implications on the overall complexity and computational requirements of the next-generation video codec. Increased Complexity: If certain advanced tools consistently show higher selection rates, the overall complexity of the codec may increase. These tools may require more sophisticated algorithms, data-driven models, or decoder-side processing, leading to a more intricate encoding and decoding process. Computational Demands: Tools with higher selection rates may also impose greater computational demands on both the encoder and decoder. More complex tools may require additional processing power, memory resources, and optimized algorithms to ensure real-time performance and efficiency. Resource Allocation: The trends in tool selection rates can influence resource allocation within the codec. Tools that are frequently selected may need more dedicated resources, while less utilized tools may be optimized or phased out to streamline the codec's operation. Adaptation and Optimization: Understanding the implications of tool selection trends can guide the adaptation and optimization of the codec. By focusing on tools that offer the most significant bitrate savings or performance gains, developers can prioritize enhancements that align with the codec's objectives. In essence, the observed trends in tool selection rates provide valuable insights into the design and implementation of the next-generation video codec, helping to balance complexity, efficiency, and performance.

How can the insights from this quantitative analysis be leveraged to guide the development of novel, data-driven coding tools that can further enhance the compression efficiency of the next-generation video codec?

The insights from the quantitative analysis offer valuable guidance for the development of novel, data-driven coding tools aimed at enhancing the compression efficiency of the next-generation video codec. Here are some ways these insights can be leveraged: Identifying Performance Gaps: By analyzing the selection rates of existing tools, developers can identify performance gaps and areas where new data-driven tools can offer improvements. Tools with lower selection rates but potential for bitrate savings can be targeted for enhancement. Optimal Tool Combinations: Understanding the interactions between coding tools can help in designing optimal combinations of tools that complement each other. Data-driven tools can be developed to work in synergy with existing tools to maximize compression efficiency. Training Data Selection: Insights from the analysis can guide the selection of training data for data-driven tools. By focusing on the types of content and scenarios where existing tools show limitations, developers can create training datasets that address specific coding challenges. Algorithm Refinement: The statistical behaviors of coding tools can inform the refinement of algorithms for data-driven tools. By studying how tools perform under different conditions, developers can fine-tune algorithms to adapt to various content characteristics and coding scenarios. Standardization Considerations: The insights can also be valuable for standardization efforts, providing evidence for the effectiveness of new data-driven tools and their potential impact on compression efficiency. This can help in advocating for the inclusion of innovative tools in the next-generation codec standard. In conclusion, leveraging the insights from the quantitative analysis can pave the way for the development of advanced data-driven coding tools that push the boundaries of compression efficiency in the next-generation video codec.
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