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eess.IV2024
Universal End-to-End Neural Network for Lossy Image Compression
Bouzid Arezki, Fangchen Feng, Anissa Mokraoui
This paper presents variable bitrate lossy image compression using a VAE-based neural network. An adaptable image quality adjustment strategy is proposed. The key innovation involv…
eess.IV2024
Convolutional Transformer-Based Image Compression
Bouzid Arezki, Fangchen Feng, Anissa Mokraoui
In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies…
eess.IV2024
Efficient Image Compression Using Advanced State Space Models
Bouzid Arezki, Anissa Mokraoui, Fangchen Feng
Transformers have led to learning-based image compression methods that outperform traditional approaches. However, these methods often suffer from high complexity, limiting their p…