83 citations · 89 across the 6 of their papers we have counts for
6 papers
Adapting Learned Image Codecs to Screen Content via Adjustable Transformations
H. Burak Dogaroglu, A. Burakhan Koyuncu, Atanas Boev +2
As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performanc…
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
Panqi Jia, A. Burakhan Koyuncu, Jue Mao +9
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in th…
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
Panqi Jia, Jue Mao, Esin Koyuncu +6
Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate…
Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression
A. Burakhan Koyuncu, Panqi Jia, Atanas Boev +2
Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achi…
Learning-Based Conditional Image Coder Using Color Separation
Panqi Jia, Ahmet Burakhan Koyuncu, Georgii Gaikov +3
Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN…
Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression
A. Burakhan Koyuncu, Han Gao, Atanas Boev +3
Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpa…