6 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression
Mohsen Jenadeleh, Jon Sneyers, Panqi Jia +3
Learning-based image compression methods have recently emerged as promising alternatives to traditional codecs, offering improved rate-distortion performance and perceptual quality…
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…
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…