Transforming Non-Progressive Learned Image Compression Models into Progressive Ones with Double-Tail-Drop Training

Core Concepts

ProgDTD, a training method that transforms non-progressive learned image compression models into progressive ones without adding any parameters or complexity.

Abstract

The paper introduces ProgDTD, a training method that transforms non-progressive learned image compression models into progressive ones. The key idea is to modify the training steps to enforce the model to store the data in the bottleneck sorted by priority, allowing for progressive transmission.
The authors apply ProgDTD to the Ballé hyperprior model, a widely adopted state-of-the-art learned image compression architecture. ProgDTD utilizes a double-tail-drop technique, where the training process drops a random percentage of the least important filters in both the latent and hyperlatent bottlenecks.
The experimental results show that ProgDTD performs comparably to its non-progressive counterparts and other state-of-the-art progressive models in terms of MS-SSIM and accuracy, with a slight drop in PSNR. ProgDTD has a customizable range of progressiveness, allowing users to balance the trade-off between the degree of progressiveness and compression performance.
The authors also evaluate the performance of ProgDTD in the context of image classification tasks, demonstrating its benefits for edge computing scenarios with variable network bandwidth.

Stats

The paper does not provide specific numerical data points, but rather presents performance curves comparing ProgDTD, non-progressive Ballé, and other state-of-the-art models in terms of MS-SSIM, PSNR, and classification accuracy.

Quotes

"ProgDTD is a training method that transforms non-progressive learned image compression models into progressive ones and does not add any parameters or complexity to the base model."
"Our experimental results show that ProgDTD performs comparably to its non-progressive counterparts and other state-of-the-art progressive models in terms of MS-SSIM and accuracy."

Key Insights Distilled From

ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training

by Ali Hojjat,J... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2305.02145.pdf

Deeper Inquiries

How can ProgDTD be extended to work with other types of learned image compression models beyond the Ballé hyperprior architecture

ProgDTD can be extended to work with other types of learned image compression models beyond the Ballé hyperprior architecture by adapting the double-tail-drop training method to suit the specific architecture of the model. Since ProgDTD is designed to prioritize data storage in the bottleneck based on importance, this concept can be applied to various compression models that have bottleneck structures. By identifying the most critical information within the bottleneck and transmitting it in order of significance, ProgDTD can be tailored to work with different neural network architectures used for image compression. The key is to understand the architecture of the specific model and modify the training steps to enforce the model to store and transmit data in a progressive manner.

What are the potential limitations or drawbacks of the tail-drop approach used in ProgDTD, and how could they be addressed in future work

The tail-drop approach used in ProgDTD may have some limitations or drawbacks that need to be addressed in future work. One potential limitation is that by dropping filters from the tail of the bottleneck during training, the model may not fully utilize the capacity of the bottleneck, leading to a decrease in performance, especially in terms of metrics like PSNR. To address this, future work could explore alternative training strategies that balance the importance of all filters in the bottleneck, ensuring that all information is utilized effectively. Additionally, the scalability of progressiveness in ProgDTD may need to be optimized to achieve a balance between performance and flexibility. Fine-tuning the range of progressiveness could help mitigate any trade-offs between scalability and performance.

Could the principles of ProgDTD be applied to other domains beyond image compression, such as video compression or other types of data compression

The principles of ProgDTD could potentially be applied to other domains beyond image compression, such as video compression or other types of data compression. By adapting the concept of prioritizing and transmitting data based on importance, similar progressive compression techniques could be developed for video data. This could involve modifying the training steps of video compression models to enforce a progressive transmission of video frames based on their significance. Additionally, the principles of ProgDTD could be explored in the context of data compression for different types of data, such as audio or text. By identifying the most critical information in the data and transmitting it in a progressive manner, the principles of ProgDTD could be adapted to various compression domains to improve efficiency and performance.