PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Ekta Prashnani*  Hong Cai*  Yasamin Mostofi  Pradeep Sen 
University of California, Santa Barbara
*joint first authors


The ability to estimate the perceptual error between images is an important problem in computer vision with many applications. Although it has been studied extensively, however, no method currently exists that can robustly predict visual differences like humans. Some previous approaches used hand-coded models, but they fail to model the complexity of the human visual system. Others used machine learning to train models on human-labeled datasets, but creating large, high-quality datasets is difficult because people are unable to assign consistent error labels to distorted images. In this paper, we present PieAPP, a metric that predicts perceptual error of a distorted image with respect to a reference in a manner consistent with human observers.

Since it is much easier for people to compare two given images and identify the one more similar to a reference than to assign quality scores to each, we propose a new, large-scale dataset labeled with the probability that humans will prefer one image over another. We then train a deep-learning model using a novel, pairwise-learning framework to predict the preference of one distorted image over the other. Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels. The perceptual error estimated by PieAPP is well-correlated with human opinion. Furthermore, it significantly outperforms existing algorithms, while also generalizing to new kinds of distortions, unlike previous learning-based methods.

Performance evaluation

We evaluate the performance of popular and state-of-the-art approaches for image error or quality prediction on our proposed test set, using Kendall, Pearson, and Spearman correlation coefficients (KRCC, PLCC, and SRCC respectively in the table below). This test set is completely disjiont from our proposed training set in terms of reference images and distortion types (total 40 test reference images and 31 test distortion types). This enables us to evaluate the generalizability of the learning-based approaches to unseen distortion types and images. More details can be found in our paper and supplementary.

MAPE 0.233 0.278 0.170 0.290
MRSE 0.185 0.211 0.135 0.219
NQM 0.216 0.234 0.508 0.246
IFC 0.165 0.178 0.230 0.198
VIF 0.179 0.192 0.250 0.212
MAD 0.270 0.301 0.231 0.304
RFSIM 0.217 0.246 0.368 0.247
GSM 0.324 0.376 0.356 0.378
SR-SIM 0.296 0.356 0.352 0.358
MDSI 0.280 0.336 0.349 0.350
MAE 0.252 0.289 0.302 0.302
RMSE 0.289 0.339 0.324 0.351
SSIM 0.272 0.323 0.245 0.316
MS-SSIM 0.275 0.325 0.051 0.321
GMSD 0.250 0.291 0.242 0.297
PSNR-HMA 0.245 0.274 0.310 0.281
FSIMc 0.322 0.377 0.481 0.378
SFF 0.258 0.295 0.025 0.305
SCQI 0.303 0.364 0.267 0.360
DOG-SSIMc 0.263 0.320 0.417 0.464
Lukin et al. 0.290 0.396 0.496 0.386
Kim et al. (CVPR 2017) 0.211 0.240 0.172 0.252
Bosse et al. NR (TIP 2018) 0.269 0.353 0.439 0.352
Bosse et al. FR (TIP 2018) 0.414 0.503 0.568 0.537
LPIPS: VGG-lin (CVPR 2018) 0.503 0.625 0.654 0.641
LPIPS: Alex-lin (CVPR 2018) 0.485 0.601 0.644 0.625
LPIPS: Squeeze-lin (CVPR 2018) 0.500 0.624 0.678 0.647
PieAPPv0.1 (CVPR 2018) 0.668 0.815 0.842 0.831

Note: For learning-based approaches, the trained models or source code released by the authors and the prescribed testing methodology were used.

Paper and Additional Resources to try PieAPPv0.1

Technical details about PieAPPv0.1:

[CVPR 2018 paper]     [CVPR 2018 supplementary document]     [CVPR 2018 poster]

Try out PieAPPv0.1:

[Code on Github]     [Win64 command-line executable]


author = {Prashnani, Ekta and Cai, Hong and Mostofi, Yasamin and Sen, Pradeep},
title = {PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}

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This project was supported in part by NSF grants IIS-1321168 and IIS-1619376, as well as a Fall 2017 AI Grant (awarded to Ekta Prashnani).