Search Results for author:
Found 5 papers, 0 papers with code
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression
Adversarial training has been validated in image compression models as a common method to enhance model robustness.
Universal Learned Image Compression With Low Computational Cost
Recently, learned image compression methods have developed rapidly and exhibited excellent rate-distortion performance when compared to traditional standards, such as JPEG, JPEG2000 and BPG.
Transformations in Learned Image Compression from a Modulation Perspective
In the view of TSM, the existing transformation methods are mathematically reduced to a linear modulation.
Exploring Structural Sparsity in Neural Image Compression
Neural image compression have reached or out-performed traditional methods (such as JPEG, BPG, WebP).
Universal Efficient Variable-rate Neural Image Compression
Recently, Learning-based image compression has reached comparable performance with traditional image codecs(such as JPEG, BPG, WebP).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
