The paper titled "Neural Video Compression with Diverse Contexts" addresses the issue of coding efficiency in video codecs. It emphasizes the importance of finding relevant contexts from previous reconstructed signals for effective encoding. While traditional codecs have shown that more contexts lead to significant coding gains, they are time-consuming. On the other hand, emerging neural video codecs (NVC) have limited contexts, resulting in a low compression ratio. To address this limitation and enhance NVC, the authors propose increasing context diversity in both temporal and spatial dimensions. They introduce a hierarchical quality pattern learning approach that enables the model to capture long-term and high-quality temporal contexts across frames. Additionally, they leverage optical flow-based coding frameworks by introducing group-based offset diversity, which enhances context mining through cross-group interaction. Furthermore, the paper adopts a quadtree-based partitioning technique to increase spatial context diversity during parallel encoding of latent representations. Experimental results demonstrate that their codec achieves a 23.5% bitrate saving compared to previous state-of-the-art NVC approaches. Moreover, their codec outperforms next-generation traditional codecs/ECMs in terms of PSNR for both RGB and YUV420 colorspaces. The authors provide the implementation codes for their codec at https://github.com/microsoft/DCVC. The paper has been accepted by CVPR 2023.
The paper is about making videos smaller without losing quality. It talks about how important it is to find the right information to make videos smaller. Regular ways of making videos smaller take a long time but work better with more information. New ways of making videos smaller don't have enough information, so they don't make the videos as small. The proposed solution is to use different kinds of information in different parts of the video. They tested this idea and found that it made videos 23.5% smaller compared to other ways. This new way also works better than other ways for certain colors in the video. You can find the code for this new way on a website called GitHub."
Definitions
- Coding efficiency: How well a video can be compressed without losing quality.
- Video codecs: Software or devices that compress and decompress digital video.
- Contexts: Relevant information or details used for effective encoding.
- Compression ratio: The amount by which a file's size is reduced when compressed.
- Temporal and spatial dimensions: Different aspects related to time and space in a video.
- Optical flow-based coding frameworks: Techniques that use motion estimation to improve video compression.
- Quadtree-based partitioning technique: A method that divides an image into smaller parts for compression purposes.
- Bitrate saving: Reducing the amount of data needed to transmit or store a video file.
- State-of-the-art NVC approaches: The most advanced methods currently available for neural video codecs.
- PSNR (Peak
Neural Video Compression with Diverse Contexts
Video compression is an important part of modern digital media, allowing for the efficient storage and transmission of video content. Traditional codecs have achieved impressive coding gains by utilizing a wide range of contexts from previous reconstructed signals. However, these codecs are time-consuming and require significant computational resources. On the other hand, emerging neural video codecs (NVC) have limited contexts, resulting in a low compression ratio.
In order to address this limitation and enhance NVC performance, the authors of “Neural Video Compression with Diverse Contexts” propose increasing context diversity in both temporal and spatial dimensions. Their paper has been accepted by CVPR 2023 and provides implementation codes for their proposed codec at https://github.com/microsoft/DCVC.
Hierarchical Quality Pattern Learning
The authors introduce a hierarchical quality pattern learning approach that enables the model to capture long-term and high-quality temporal contexts across frames. This approach leverages optical flow-based coding frameworks by introducing group-based offset diversity which enhances context mining through cross-group interaction. Additionally, it adopts a quadtree-based partitioning technique to increase spatial context diversity during parallel encoding of latent representations.
Experimental Results
Experimental results demonstrate that their proposed codec achieves 23% bitrate saving compared to previous state-of-the-art NVC approaches as well as outperforming next generation traditional codecs/ECMs in terms of PSNR for both RGB and YUV420 colorspaces .
Conclusion
In conclusion, “Neural Video Compression with Diverse Contexts” proposes an effective solution to improve coding efficiency in video codecs while reducing computational complexity associated with traditional methods through increased context diversity in both temporal and spatial dimensions. The authors provide implementation codes for their proposed method at https://github.com/microsoft/DCVC which can be used to further explore its potential applications within the field of video compression technology
For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://github.com/microsoft/DCVC.
