SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning
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RESEARCH ARTICLE
SUR‑FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning Hanhe Lin1 · Vlad Hosu1 · Chunling Fan2 · Yun Zhang2 · Yuchen Mu3 · Raouf Hamzaoui4 · Dietmar Saupe1 Received: 1 December 2019 © Springer Nature Switzerland AG 2020
Abstract The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson–Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays. Keywords Just noticeable difference · Satisfied user ratio · Deep learning · Image compression
Introduction Image compression is typically used to meet constraints on transmission bandwidth and storage space. The quality of a compressed image is quantitatively determined by encoding parameters, e.g., the quality factor (QF) in JPEG compression. When images are compressed, artifacts such as blocking and ringing may appear and affect the visual quality experienced by the users. The satisfied user ratio (SUR) is the fraction of users that do not perceive any distortion * Hanhe Lin hanhe.lin@uni‑konstanz.de 1
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
2
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3
School of Engineering, University of Edinburgh, Edinburgh, UK
4
School of Engineering and Sustainable Development, De Montfort University, Leicester, UK
when comparing the original image to its compressed version. The constraint on the SUR may vary according to the application. Determining the relationship between the encoding parameter and the SUR is a challenging task. The conventional method consists of three st
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