Subjective quality assessment IRCCyN/IVC database

By P. Le Callet, Florent Autrusseau. 2004

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b9b1e380bf519f8068fef85eccf8e3ccdb9ebf5a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “9129 IVC database [129], Toyama-MICT database [130], TID2008 [131], CSIQ database [132]”
A reduced-reference perceptual image and video quality metric based on edge preservation (2012, methodology): “The second database, IRCCyN/IVC [24], was developed by the Institut de Recherche en Communications et Cyberntique de Nantes .”
Seven Challenges in Image Quality Assessment: Past, Present, and Future Research (2013, methodology): “The IRCCyN/IVC database [121, 122], developed at the Institut de Recherche en Communications et Cybernétique de Nantes (IRCCyN), France, contains 10 reference images and 185 distorted images in 24-bpp color BMP format at an image resolution of 512 × 512 pixels.”
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment (2019, methodology): “The early, conventionally built IQA database IVC [17] was released in 2005.”
Group MAD Competition? A New Methodology to Compare Objective Image Quality Models (2016, background): “Widely recognized image databases with subjective ratings include LIVE [27], TID2008 [23], TID2013 [22], CSIQ [12], IVC [13], Toyama-MICT [9] and VCL@FER [41].”
Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, unknown): “Low-light enhancement: The datasets used to benchmark enhancement works are commonly taken from data used for quality evaluation, but not necessarily a standard that is widely used in the low-light domain, such as the IVC database ([9]) that were collected for other image enhancement works instead o”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, methodology): “To extract fog aware statistical features from a corpus of fog-free images, we selected 500 natural fog-free images from the LIVE IQA database [47], the Berkeley image segmentation database [48], the IRCCyN/IVC database [49], and the CSIQ database [50].”
Analysis of Public Image and Video Databases for Quality Assessment (2012, unknown): “• IRCCyN/IVC Image Quality Database [12], [13].”
KADID-10k: A Large-scale Artificially Distorted IQA Database (2019, unknown): “of Ratings Subjective study Database Year Content distorted images Distortion type distortion types rated images per image environment IVC [5] 2005 10 185 artificial 4 185 15 lab LIVE [6] 2006 29 779 artificial 5 779 23 lab CSIQ [7] 2009 30 866 artificial 6 866 5∼7 lab TID2013 [2] 2013 25 3,000 arti”
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models (2017, unknown): “Other widely known but smaller databases include IVC [24], Toyama-MICT [25], Cornell A57 [26] and WIQ [27], etc.”