Category Archives: Scientific Publications

Artificial Intelligence (AI) smart glasses with integrated cameras are becoming increasingly prevalent, yet their image quality remains underexplored. This study presents a comprehensive evaluation protocol tailored for such human-facing wearable devices, using both standardized and proprietary metrics (including AI based metrics). Key metrics include Local Contrast Gain (LCG), face exposure, texture preservation, visual noise in […]

This work provides a novel glass-to-glass metric of local contrast, useful in the context of image quality evaluation of HDR content. This metric, called Local-Contrast Gain (LCG), uses the opto-optical transfer function (OOTF) of the imaging system and its first derivative to compute the incremental ratio between contrast in the scene and contrast on the […]

This paper is the continuation of a previous work in [1], which aimed to develop a color rendering model using ICtCp color space, to evaluate SDR and HDR-encoded content. However, the model was only tested on an SDR image dataset. The focus of this paper is to provide an analysis of a new HDR dataset […]

A. Tigranyan, P. Mathieu, C. Nannini, F. Thomas, M. Patti, F. Guichard This article provides elements to answer the question: How to judge general stylistic color rendering choices made by imaging devices capable of recording HDR formats in an objective manner? The goal of our work is to build a framework to analyze color rendering […]

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). 

The classic window pattern with a black background is the basis of any display characterization, but nowadays, displays, particularly smartphone displays, integrate complex image processing and adaptations, meaning that they cannot be entirely characterized with these simple patterns. 

The number of cameras designed for capturing the nearinfrared (NIR) spectrum (sometimes in addition to the visible) is increasing in automotive, mobile, and surveillance applications. Therefore, NIR LED light sources have become increasingly present in our daily lives. Nevertheless, camera evaluation metrics are still mainly focused on sensors in the visible spectrum. 

In this paper, we propose a rating protocol for evaluating smartphone audio zoom systems through objective and perceptual testing. Audio zoom is a newly developed function that helps isolate a sound source from its surroundings in accordance with the smartphone camera’s focal point and zoom level when recording videos with the camera app. 

The wide use of cameras by the public has raised the interest of image quality evaluation and ranking. Current cameras embed complex processing pipelines that adapt strongly to the scene content by implementing, for instance, advanced noise reduction or local adjustment on faces. 

Nowadays, cameras are widely used to detect potential obstacles for driving assistance. The safety challenges have pushed the automotive industry to develop a set of image quality metrics to measure the intrinsic camera performances and degradations. However, more metrics are needed to correctly estimate computer vision algorithms performance, which depends on environmental conditions. 

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA).

High Dynamic Range (HDR) videos attract industry and consumer markets thanks to their ability to reproduce wider color gamuts, higher luminance ranges and contrast. While the cinema and broadcast industries traditionally go through a manual mastering step on calibrated color grading hardware, consumer cameras capable of HDR video capture without user intervention are now available.