Transformer-based Image Compression with Variable Image Quality Objectives
This provides incremental flexibility for users in image compression by allowing trade-offs between quality objectives without sacrificing performance.
The paper tackles the problem of image compression with variable quality objectives by introducing a Transformer-based system that adapts encoding/decoding via learned prompt tokens, achieving comparable rate-distortion performance to single-objective methods.
This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with varying visual characteristics. Our method provides the user with the flexibility to choose a trade-off between two image quality objectives using a single, shared model. Motivated by the success of prompt-tuning techniques, we introduce prompt tokens to condition our Transformer-based autoencoder. These prompt tokens are generated adaptively based on the user's preference and input image through learning a prompt generation network. Extensive experiments on commonly used quality metrics demonstrate the effectiveness of our method in adapting the encoding and/or decoding processes to a variable quality objective. While offering the additional flexibility, our proposed method performs comparably to the single-objective methods in terms of rate-distortion performance.