10 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…
Structured Image-based Coding for Efficient Gaussian Splatting Compression
Pedro Martin, Antonio Rodrigues, Joao Ascenso +1
Gaussian Splatting (GS) has recently emerged as a state-of-the-art representation for radiance fields, combining real-time rendering with high visual fidelity. However, GS models r…
An Overview of the JPEG AI Learning-Based Image Coding Standard
Semih Esenlik, Yaojun Wu, Zhaobin Zhang +5
JPEG AI is an emerging learning-based image coding standard developed by Joint Photographic Experts Group (JPEG). The scope of the JPEG AI is the creation of a practical learning-b…
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…
GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis
Pedro Martin, António Rodrigues, João Ascenso +1
Gaussian Splatting (GS) offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. Using a set of 3D Gaussians to represent complex geometry…