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 …
Deep image compression based on multi-scale deformable convolution
Deep image compression efficiency has been improved in the past years. However, to fully
exploit context information for compressing image objects of different scales and shapes …
exploit context information for compressing image objects of different scales and shapes …
Exploring structural sparsity in neural image compression
The performance of neural image compression have reached or suppressed traditional
methods (such as JPEG, BPG, WebP). However, their sophisticated network structures with …
methods (such as JPEG, BPG, WebP). However, their sophisticated network structures with …
Oodhdr-codec: Out-of-distribution generalization for hdr image compression
Recently, deep learning has been proven to be a promising approach in standard dynamic
range (SDR) image compression. However, due to the wide luminance distribution of high …
range (SDR) image compression. However, due to the wide luminance distribution of high …
Hierarchical feature aggregation network for deep image compression
W Li, Z Du, H He, J Tang, G Wu - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Existing CNN-based methods for image compression extract features through serially
connected high-to-low (encoder) or low-to-high (decoder) resolution stages, leading to …
connected high-to-low (encoder) or low-to-high (decoder) resolution stages, leading to …