C3: High-performance and low-complexity neural compression from a single image or video (2312.02753v1)

Abstract: Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3, a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on COOL-CHIC (Ladune et al.) and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark, we match the RD performance of VTM, the reference implementation of the H.266 codec, with less than 3k MACs/pixel for decoding. On the UVG video benchmark, we match the RD performance of the Video Compression Transformer (Mentzer et al.), a well-established neural video codec, with less than 5k MACs/pixel for decoding.

• The paper introduces an overfitting method that tailors a small neural network to individual images or videos to achieve state-of-the-art rate-distortion performance.
• It dramatically reduces decoding complexity—by an order of magnitude relative to traditional models—making it viable for hardware-constrained applications.
• Building on COOL-CHIC, the method enhances optimization and quantization techniques, paving the way for future improvements in encoding speed and efficiency.

Analysis of "C3: High-performance and low-complexity neural compression from a single image or video"

The paper introduces C3, a neural compression method that significantly advances the field of neural compression by focusing on single-instance overfitting rather than generalization across datasets. This approach not only achieves strong rate-distortion (RD) performance but also dramatically reduces the decoding complexity typically associated with such neural methods.

Core Contributions and Methodology

1. Overfitting vs. Generalization: Unlike traditional neural compression models that require large datasets and complex architectures to generalize, C3 opts for overfitting a small model to each image or video. Such a method drastically lowers decoding complexity while maintaining comparable RD performance to state-of-the-art codecs.
2. Decoding Complexity: By reducing decoding complexity by an order of magnitude relative to existing baselines, C3 makes neural compression more viable for hardware-constrained environments, such as mobile devices.
3. Building on COOL-CHIC: C3 is an evolution of the COOL-CHIC method, incorporating several enhancements for image compression and introducing novel methods for video compression. Notably, the methodological advancements include improved optimization, quantization techniques, and architectural adjustments.
4. Image and Video Compression Benchmarks: On the CLIC2020 image benchmark, C3 matches the RD performance of VTM (H.266 codec) with less than 3k MACs/pixel, and on the UVG video benchmark, it matches the Video Compression Transformer’s performance with less than 5k MACs/pixel.

Implications and Future Directions

C3's capability to achieve VTM-level RD performance with dramatically reduced complexity challenges the status quo of neural compression. Its potential applications are expansive, particularly in fields where computational efficiency is paramount. The model's design, however, implicates long encoding times, a common constraint for optimization-driven approaches which trade off encoding speed for enhanced decoding efficiency and compression quality.

Practical Impact: The reduction in complexity without compromising RD performance opens avenues for deploying high-quality compression in real-time applications on consumer-grade hardware.

Theoretical Implications: The approach challenges conventional neural compression paradigms, highlighting a significant shift in how neural models can leverage overfitting for efficiency. This may stimulate further research into neural fields and single-instance models in other domains.

Future Developments: Further research should focus on accelerating encoding processes, perhaps via techniques like meta-learning or pre-training on representative domains. Moreover, exploration into more parallelized probabilistic models for decoding could further optimize latency.

Conclusion

C3 represents a marked step forward in neural compression, achieving a rare confluence of high RD performance and low decoding complexity. Its methodology diverges from established norms, advocating for highly efficient, instance-specific overfitting that is both innovative and practical. While challenges remain in reducing encoding times and optimizing hardware compatibility, C3 lays a foundational approach that will likely influence future compression models and their applications.