Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
Summary: arXiv:2511.06424v2 Announce Type: replace-cross
Abstract
While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents an efficient zero-shot diffusion-based compression method that runs substantially faster than existing methods, while maintaining performance that is on par with state-of-the-art techniques.
Introduction
The field of image compression has been revolutionized by the advent of zero-shot diffusion-based methods. These techniques leverage the power of diffusion processes to achieve high-quality compression; however, their computational overhead has posed challenges for practical applications. The Turbo-DDCM method aims to address these challenges by improving speed and flexibility.
Methodology
Turbo-DDCM builds upon the recently proposed Denoising Diffusion Codebook Models (DDCMs) compression scheme. The core idea of DDCM is to compress an image by sequentially choosing diffusion noise vectors from reproducible random codebooks. This process guides the denoiser’s output to reconstruct the target image effectively.
Key Innovations
- Efficiency through Noise Vector Combination: Turbo-DDCM modifies the original DDCM framework by efficiently combining a large number of noise vectors at each denoising step. This significantly reduces the total number of required denoising operations, enhancing processing speed.
- Improved Encoding Protocol: Alongside the noise vector combination, Turbo-DDCM incorporates an improved encoding protocol that optimizes the overall compression process.
- Flexible Variants: Two flexible variants of Turbo-DDCM are introduced:
- Priority-aware Variant: This variant allows prioritization of user-specified regions within an image, ensuring that critical areas maintain higher quality during compression.
- Distortion-controlled Variant: Unlike traditional methods that focus on Bits Per Pixel (BPP), this variant compresses images based on a target Peak Signal-to-Noise Ratio (PSNR), providing a more tailored approach to quality control.
Experimental Results
Comprehensive experiments demonstrate that Turbo-DDCM not only accelerates the compression process but also maintains performance levels comparable to existing state-of-the-art techniques. The flexibility offered by the two variants allows users to adapt the compression strategy according to specific requirements, making Turbo-DDCM a compelling choice for various applications.
Conclusion
In conclusion, Turbo-DDCM represents a significant advancement in the field of image compression. By enhancing the efficiency and flexibility of zero-shot diffusion-based methods, it paves the way for more practical applications in real-world scenarios. With its innovative approach and strong performance, Turbo-DDCM is poised to make a lasting impact on the landscape of image compression technologies.