EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation

Core Concepts

Enhanced generative image compression method EGIC efficiently traverses the distortion-perception curve using semantic segmentation guidance and output residual prediction.

Abstract

EGIC introduces a novel approach to generative image compression, combining semantic segmentation guidance and output residual prediction for efficient traversal of the distortion-perception curve. The method outperforms state-of-the-art diffusion and GAN-based methods while being simple to implement and storage-efficient. By incorporating OASIS-C and ORP, EGIC provides excellent interpolation characteristics, making it suitable for practical applications targeting low bit ranges.

Stats

DIRAC-100 achieves 0.157bpp at 1.11x PSNR. HiFiC operates at 0.172bpp with 1.08x PSNR. MS-ILLM performs at 0.164bpp with 1.03x PSNR. EGIC achieves 0.159bpp with α=1.0. EGIC also reaches 0.159bpp with α=0.0.

Quotes

"EGIC forms a powerful codec, outperforming state-of-the-art diffusion and GAN-based methods." "ORP is a lightweight retrofit solution for multi-realism image compression." "OASIS-C provides spatially and semantically-aware gradient feedback to the generator."

Key Insights Distilled From

EGIC

by Niko... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2309.03244.pdf

Deeper Inquiries

How does EGIC's efficiency in traversing the distortion-perception curve impact its practical applications

EGIC's efficiency in traversing the distortion-perception curve significantly impacts its practical applications by offering a versatile solution for image compression. The ability to efficiently navigate this curve from a single model allows for fine-tuning the trade-off between distortion and perception based on specific requirements. This flexibility is crucial in various real-world scenarios where different levels of image quality are needed, such as in storage-efficient applications or bandwidth-constrained environments. By providing excellent interpolation characteristics and outperforming state-of-the-art methods while being lightweight and simple to implement, EGIC becomes a promising candidate for practical use cases targeting the low bit range.

What are the potential limitations of relying on semantic segmentation guidance in generative image compression

While semantic segmentation guidance can enhance generative image compression performance, there are potential limitations to consider. One limitation is the reliance on labeled training data for semantic segmentation models, which may not always be readily available or require significant effort to annotate. Additionally, semantic segmentation-guided discriminators may struggle with preserving small details like faces or text due to their focus on overall spatial and semantic information rather than localized features. Moreover, these models might introduce artifacts if not carefully designed or trained properly, impacting the overall quality of compressed images.

How can the concept of output residual prediction be applied to other areas beyond image compression

The concept of output residual prediction used in EGIC for multi-realism image compression can be applied beyond image compression in various domains that involve synthesis tasks requiring control over realism levels. For instance: In video processing: Output residual prediction could help adjust the level of detail added during video frame generation based on specific criteria like motion complexity or scene content. In natural language processing: Residual prediction could aid in generating diverse outputs with varying degrees of formality or style by controlling how much additional linguistic nuances are incorporated into generated text. In audio synthesis: Similar techniques could be employed to manipulate sound generation processes by adjusting residual components between different optimized decoders, allowing users to customize audio outputs based on desired characteristics like clarity or richness. By adapting output residual prediction methodologies across different domains, it becomes possible to achieve nuanced control over synthesized outputs tailored to specific application needs.