Neural Image Compression with Text-guided Encoding for Pixel-level and Perceptual Fidelity

Core Concepts

The author develops a text-guided image compression algorithm that achieves high perceptual and pixel-wise fidelity by leveraging text information mainly through text-adaptive encoding and training with joint image-text loss.

Abstract

Recent advances in text-guided image compression aim to enhance perceptual quality while maintaining pixel-wise fidelity. The proposed algorithm, TACO, utilizes text information for encoding to achieve high-quality reconstructions. Experimental results show superior performance compared to baselines in terms of LPIPS, PSNR, and FID across various datasets.
Key points include the importance of utilizing text for encoding rather than decoding, the development of a simple yet effective encoder-centric compression algorithm called TACO, and the achievement of high perceptual and pixel-level quality simultaneously. The study compares TACO with different codecs and highlights its effectiveness in preserving textual information while achieving competitive compression results.
The paper addresses limitations such as computational cost scaling with sequence length and scarcity of image-text datasets for training. Future work includes exploring captioning models for generating training data. Further analyses compare TACO's adapter architecture with other methods, evaluate preservation of textual information, assess computational costs, and examine the impact of different captioning models on compression quality.

Stats

We use all five captions for training.
For evaluation, we center-crop images to 256x256 resolution.
We train models with λ ∈ {4, 8, 16, 40, 90, 150} × 10−4.
We average over 100 MS-COCO images at 0.0148 bpp.
We use Adam with batch size 4 and train for 50 epochs.
Initial learning rate set to 10^-4; decay LR by 1/10 at epochs 45 and 48.

Quotes

"Utilizing text to guide the decoding procedure may be much more challenging task."
"Our findings suggest that the core value of text in image compression may hinge more on its relationship with human perception."
"TACO outperforms all baselines in terms of LPIPS across various datasets."
"The proposed text adapter is indeed much more effective than what TGIC uses."
"TACO-compressed images tend to preserve textual information much better than PSNR-focused methods."

Key Insights Distilled From

Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity

by Hagyeong Lee... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02944.pdf

Deeper Inquiries

How can utilizing text for encoding improve image compression compared to traditional decoding methods?

Utilizing text for encoding in image compression, as demonstrated by TACO, offers several advantages over traditional decoding methods:

Preservation of Perceptual and Pixel-level Fidelity: By injecting textual information into the encoder, TACO can achieve high perceptual and pixel-wise fidelity simultaneously. This is crucial as traditional decoding methods tend to prioritize one aspect over the other.
Effective Knowledge Injection: The text adapter architecture used in TACO allows for efficient injection of semantic information from the text into the image encoder. This ensures that relevant details are preserved during compression.
Global Semantic Understanding: Text-guided encoding enables a global understanding of semantic information present in the text, which can be utilized more effectively during compression compared to local region-based approaches often seen in traditional decoding methods.

In summary, utilizing text for encoding enhances image compression by improving fidelity, enabling effective knowledge injection, and enhancing global semantic understanding.

What are the implications of preserving textual information in compressed images for downstream applications?

Preserving textual information in compressed images has significant implications for downstream applications:

Improved Image Retrieval: Preserved textual information allows for better indexing and retrieval of compressed images based on their content descriptions.
Enhanced Accessibility: Retaining textual context within compressed images makes it easier to understand their content without fully decompressing them. This can benefit applications where quick access to relevant visual data is essential.
Semantic Search Capabilities: The preservation of textual metadata facilitates advanced search functionalities based on both visual features and associated descriptive texts.

Overall, preserving textual information in compressed images enhances accessibility, searchability, and overall utility across various downstream applications.

How can TACO's approach be adapted or improved upon to address scalability issues related to long text sequences during encoding?

To address scalability issues related to long text sequences during encoding with TACO's approach, several adaptations or improvements could be considered:

Hierarchical Encoding: Implement a hierarchical approach where long texts are processed at different levels (e.g., word-level embedding followed by sentence-level aggregation) before being injected into the encoder.
Attention Mechanism Optimization: Optimize attention mechanisms within the text adapter module to focus more efficiently on relevant parts of long texts while reducing computational overhead.
Parallel Processing: Explore parallel processing techniques that allow simultaneous handling of different segments of long texts within the encoder architecture without sacrificing performance or speed.

By incorporating these adaptations or improvements into TACO's approach, scalability issues related to processing long text sequences during encoding can be effectively mitigated while maintaining high-quality image compression results.