7 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…
Evaluation of Objective Image Quality Metrics for High-Fidelity Image Compression
Shima Mohammadi, Mohsen Jenadeleh, Jon Sneyers +2
Nowadays, image compression solutions are increasingly designed to operate within high-fidelity quality ranges, where preserving even the most subtle details of the original image…
In-place Double Stimulus Methodology for Subjective Assessment of High Quality Images
Shima Mohammadi, Mohsen Jenadeleh, Michela Testolina +4
This paper introduces a novel double stimulus subjective assessment methodology for the evaluation of high quality images to address the limitations of existing protocols in detect…
Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto +10
High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, result…
The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future
Jon Sneyers, Jyrki Alakuijala, Luca Versari +9
JPEG XL is a new image coding system offering state-of-the-art compression performance, lossless JPEG recompression, and advanced features. It aims to replace JPEG, PNG, GIF, and o…
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…