Learning-driven lossy image compression: A comprehensive survey
In the field of image processing and computer vision (CV), machine learning (ML)
architectures are widely used. Image compression problems can be solved using …
architectures are widely used. Image compression problems can be solved using …
Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding
Recently, learned image compression techniques have achieved remarkable performance,
even surpassing the best manually designed lossy image coders. They are promising to be …
even surpassing the best manually designed lossy image coders. They are promising to be …
Learned image compression with mixed transformer-cnn architectures
Learned image compression (LIC) methods have exhibited promising progress and superior
rate-distortion performance compared with classical image compression standards. Most …
rate-distortion performance compared with classical image compression standards. Most …
Hst: Hierarchical swin transformer for compressed image super-resolution
Abstract Compressed Image Super-resolution has achieved great attention in recent years,
where images are degraded with compression artifacts and low-resolution artifacts. Since …
where images are degraded with compression artifacts and low-resolution artifacts. Since …
Image coding for machines with omnipotent feature learning
Abstract Image Coding for Machines (ICM) aims to compress images for AI tasks analysis
rather than meeting human perception. Learning a kind of feature that is both general (for AI …
rather than meeting human perception. Learning a kind of feature that is both general (for AI …
Joint graph attention and asymmetric convolutional neural network for deep image compression
Recent deep image compression methods have achieved prominent progress by using
nonlinear modeling and powerful representation capabilities of neural networks. However …
nonlinear modeling and powerful representation capabilities of neural networks. However …
Mlic: Multi-reference entropy model for learned image compression
Recently, learned image compression has achieved remarkable performance. The entropy
model, which estimates the distribution of the latent representation, plays a crucial role in …
model, which estimates the distribution of the latent representation, plays a crucial role in …
[PDF][PDF] Overview of Intelligent Signal Processing Systems
ABSTRACT Niklaus Emil Wirth introduced the innovative concept of Programming=
Algorithm+ Data Structure [109]. Inspired by this, we advance the concept to the next level by …
Algorithm+ Data Structure [109]. Inspired by this, we advance the concept to the next level by …
Complexity-guided slimmable decoder for efficient deep video compression
In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in
combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression …
combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression …
Content adaptive latents and decoder for neural image compression
In recent years, neural image compression (NIC) algorithms have shown powerful coding
performance. However, most of them are not adaptive to the image content. Although …
performance. However, most of them are not adaptive to the image content. Although …