1 citations · 1 across the 5 of their papers we have counts for
5 papers
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
Mohsen Jenadeleh, Jon Sneyers, João Ascenso +8
Recent advances in conventional and learning-based image coding have increased the demand for benchmark datasets that support fine-grained assessment of compressed image quality, p…
On Optimizing Image Codecs for VMAF NEG: Analysis, Issues, and a Robust Loss Proposal
Florian Fingscheidt, Alexander Karabutov, Panqi Jia +2
The VMAF (video multi-method assessment fusion) metric for image and video coding recently gained more and more popularity as it is supposed to have a high correlation with human p…
Overview of Variable Rate Coding in JPEG AI
Panqi Jia, Fabian Brand, Dequan Yu +3
Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-ba…
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…