TID2008 – A database for evaluation of full-reference visual quality assessment metrics

By Nikolay N. Ponomarenko, Vladimir Vasilyevich Lukin, Alexander A. Zelensky. 2004

1,188 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Amortised MAP Inference for Image Super-resolution (2016, methodology): “Conveniently, if A is a strided two-dimensional convolution, then A + becomes a deconvolution or up-convolution, which is a standard operation used in deep learning (e. g. Shi et al., 2016).”
End-to-end optimization of nonlinear transform codes for perceptual quality (2016, neutral): “We find that for purposes of image compression, one iteration is sufficient: x̂ = gs(ŷ;θ) s.t. x̂ = H ′w and wi = ŷi · ( β′i + ∑ j γ′ij |ŷj |α ′ ij )ε′i , (6) where the parameter vector consists of a distinct set of parameters: θ = {α′,β′,γ′, ε′,H ′}.”
Eigen-Distortions of Hierarchical Representations (2017, neutral): “We trained all of the models on the TID-2008 database, which contains a large set of original and distorted images, along with corresponding human ratings of perceived distortion [Ponomarenko et al., 2009].”
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “However, unlike the TID datasets [42, 43], we do not explicitly convert this preference into a quality score, since approaches such as Swiss tournaments introduce errors and do not scale.”
A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images (2015, methodology): “Experimental results show that the proposed PCQI method is well correlated with subjective evaluations of image quality, suggesting that the proposed model is promising at handling contrast changed images.”
Image database TID 2013 : Peculiarities , results and perspectives (2016, methodology): “Practice of analysis for visual quality metric using TID2008 has demonstrated that it is reasonable to study MOS estimated for all types of distortions as well as for particular subsets [11,16,43].”
Analysis of Public Image and Video Databases for Quality Assessment (2012, background): “) are preferable to collect realistic MOS data [25], [59].”
No-Reference Image Quality Assessment in the Spatial Domain (2012, background): “Examples of (a) natural images and (b) artificial images from the TID database [4].”
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, result): “Models Type CID2013 [33] CCID2014 [30] TID2008 [22] CSIQ [23] PLC SRC KRC PLC SRC KRC PLC SRC KRC PLC SRC KRC FSIM FR 0.”
Blind image quality evaluation using perception based features (2015, methodology): “The performance of PIQUE is evaluated on three of the largest publicly available databases that have human subjective scores, LIVE [18], CSIQ [19] and TID2008 [20].”
On the use of deep learning for blind image quality assessment (2016, methodology): “mages with a high agreement with respect to human subjective scores. Furthermore, we show the applicability of our method to the legacy LIVE Image Quality Assessment Database [41], CSIQ [23], TID2008 [37] and TID2013 [36]. The rest of the paper is organized as follows: Section II introduces the prop”
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment (2019, methodology): “LIVE [18], TID2008 [19], and CSIQ [20] were the most commonly used to develop, improve, and evaluate objective IQA methods.”
Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework (2017, methodology): “To evaluate the generalization of DeepQA models, they were trained using the LIVE IQA database, and tested on the TID2008 database.”