Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis

Concetti Chiave

Enhancing image compression through a conditional diffusion-based decoder and an innovative entropy model.

Sintesi

Abstract

Non-isotropic diffusion model at the decoder side enhances image quality.
Novel entropy model accurately models latent representation probability distribution.

Introduction

Learning-based methods surpass classical codecs in rate-distortion performance.
Generative-based codecs aim for realistic reconstructions.

Diffusion Models

Diffusion models gradually denoise images, improving stability and quality.

Neural Entropy Model

Accurate entropy estimation crucial for compression efficiency.

Methods

Conditional blurring diffusion model improves realism of reconstructed images.

Experiments

Kodak dataset used to evaluate performance against state-of-the-art methods.

Ablation Study

Impact of maximum blurring and positional encoding on compression efficiency explored.

Conclusion

Leveraging diffusion models and advanced entropy modeling enhances image compression performance.

Statistiche

Our proposed framework yields better perceptual quality compared to cutting-edge generative-based codecs.
The proposed entropy model contributes to notable bitrate savings.

Citazioni

"Our proposed framework yields better perceptual quality compared to cutting-edge generative-based codecs."
"The proposed entropy model contributes to notable bitrate savings."

Approfondimenti chiave tratti da

Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis

by Atefeh Khosh... alle arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16258.pdf

Domande più approfondite

How can the integration of global and local spatial context enhance the precision of capturing spatial correlations

The integration of global and local spatial context enhances the precision of capturing spatial correlations by providing a comprehensive understanding of the relationships between different elements in an image. Local spatial context focuses on capturing immediate dependencies within a specific region, while global spatial context considers broader relationships across the entire image. By combining both types of context, the model can effectively capture short-range and long-range dependencies simultaneously. This approach allows for a more nuanced analysis of spatial correlations, leading to more accurate predictions and better compression efficiency.

What are the implications of using Laplacian-shaped positional encoding for computing global spatial context

Using Laplacian-shaped positional encoding for computing global spatial context has significant implications for improving compression efficiency and enhancing perceptual quality in image processing tasks. The Laplacian-shaped positional encoding offers an effective way to model long-distance dependencies by dynamically adjusting receptive fields based on learned parameters during optimization. This adaptive approach ensures that each channel chunk receives relevant information from its surroundings, enabling precise estimation of entropy and improved compression performance. Additionally, the Laplacian function's characteristics help capture complex patterns in images with varying scales, contributing to better overall compression results.

How does the use of a non-isotropic diffusion model impact the generation of high-quality images

The use of a non-isotropic diffusion model significantly impacts the generation of high-quality images by introducing an inductive bias that distinguishes between frequency components within an image. Unlike traditional diffusion models that treat all frequencies equally during decoding processes, a non-isotropic diffusion model modulates diffusion rates based on frequency importance. This approach enables coarse-to-fine generation where each frequency component undergoes diffusion at distinct rates, resulting in realistic reconstructions with enhanced details and textures. By incorporating this inductive bias into the decoder side, the model can produce high-quality images with improved perceptual levels compared to traditional methods like Gaussian decoders or isotropic diffusion models.