CNN Based No‐Reference HDR Image Quality Assessment

Chinese Journal of ElectronicsVolume 30, Issue 2 p. 282-288 Original Research PaperFree Access CNN Based No-Reference HDR Image Quality Assessment Fan Kefeng, Corresponding Author fankf@126.com Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 China School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004 ChinaSearch for more papers by this authorLiang Jiyun, Corresponding Author 18589837505@163.com Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 China School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004 ChinaSearch for more papers by this authorLi Fei, Corresponding Author fiona2022@163.com Guangzhou SequoiaDB Co., Guangzhou, 510006 ChinaSearch for more papers by this authorQiu Puye, Corresponding Author qiupy@cesi.cn Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 ChinaSearch for more papers by this author Fan Kefeng, Corresponding Author fankf@126.com Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 China School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004 ChinaSearch for more papers by this authorLiang Jiyun, Corresponding Author 18589837505@163.com Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 China School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004 ChinaSearch for more papers by this authorLi Fei, Corresponding Author fiona2022@163.com Guangzhou SequoiaDB Co., Guangzhou, 510006 ChinaSearch for more papers by this authorQiu Puye, Corresponding Author qiupy@cesi.cn Digital Technology Research Center, China Electronics Standardization Institute, Beijing, 100007 ChinaSearch for more papers by this author First published: 01 March 2021 https://doi.org/10.1049/cje.2021.01.008 This work is support by The National Key Research and Development Program of China (No.2019YFB1405503) and 2019 Public Service Platform of Industrial Technology Foundation of MIIT (No.2019-00895-2-1). AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Motivated by the problems of non-universality and over-reliance on the original reference image in High dynamic range (HDR) Image quality assessment (IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance (SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network (Error-net), a Perceptual resistance network (PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient (SROCC), Pearson product-moment correlation coefficient (PLCC) and Root mean square error (RMSE) correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods. I. Introduction With the rapid development of High dynamic range (HDR) technology, Ultra-high-definition (UHD) image displays have been further upgraded, which bring humans closer to the visual experience of real-world[1]. HDR images have richer information and require bigger storage space with respect to traditional images. And due to the limitations of existing storage technologies and network transmission resources, it is inevitable that the quality of HDR images will be distorted during the process of acquiring, compressing, storing, and transmitting. At present, the HDR Image quality assessment (IQA) algorithm has gradually become a research hotspot[2,3] under the rapid development of imaging technology, the popularization of 5G communication and the explosive emerging of AI technology. Subjective assessment is the most reliable solution for HDR image quality assessment, but it's tedious, time-consuming, highly dependent on personnel and equipment resources make it difficult to apply in most scenarios. In this context, developing a stable, reliable, simple and efficient objective quality assessment model undoubtedly is a better solution. Existing HDR image quality assessment methods can be divided into Specific IQA for HDR and IQA based on legacy SDR. The IQA based on legacy SDR means applying the traditional IQA methods directly to HDR image, such as Root mean square error (RMSE), Peak signal-to-noise ratio (PSNR), Visual information fidelity (VIF), Image signature based mean square error (ISMSE)[4] and Complementary color wavelet transform (CCWT)[5]. Nevertheless, related research shows that such methods have a poor prediction that are not suitable for HDR images[6]. Therefore, IQA for HDR in the specialty has attracted increasing attention and some related studies have also achieved good results. The proposed algorithm belongs to latter one that can evaluate the quality of HDR images without reference to HDR images. II. Related Works In 2015, Mantiuk et al.[7] improved the HDR Visual difference predictor (HDR-VDP) and proposed the HDR-VDP-2.2 that widely recognized by the industry of the IQA. The biggest advantage of this version is the optimization of frequency-based pooling strategy, which has both achieved better prediction accuracy and better predict outcomes. In 2015, Narwaria et al.[8] proposed the visual difference measurement (HDR-VQM) for the quality assessment of HDR video. The proposed algorithm takes into account the human visual system that tends to focus on a specific area within a specific time. And hence it can analyze the quality change by the image information of the specific spatial position corresponding to a certain time frequency. The method shows good performance in HDR image quality assessment. In 2017, Yu Jiaowen et al.[9] proposed a HDR image quality assessment method based on manifold learning, using manifold features to mimic HVS perception characteristics. Their algorithm not only considers the effect of luminance information, but also color information on the quality of the HDR image compared with the aforementioned method. And finally, it obtained good predictive outcomes. In 2018, Zou Liangtao et al.[10] proposed a no-reference HDR image quality assessment method based on tensor domain perception features. From the perspective of tensor domain, the author combined with image information including luminance, chroma and dynamic range. The SVR model to obtain the final quality score of HDR images is used. This method their proposed achieved superior prediction results. In 2018, Guan et al.[11] proposed a no-reference HDR image quality assessment method based on tensor decomposition and manifold learning. They maintained the internal structure of the image data by tensors. Three quantum band diagrams are obtained after tensor decomposition. The first sub-band diagram is used to extract features of the popular structure, and the latter two sub-band diagrams are used to extract detailed contrast characteristics. Their method obtains good prediction performance. In addition to the methods of Zou Liangtao and Guan, all the aforementioned methods are full-reference HDR IQA methods that require original undistorted images as reference. To the best of our knowledge, these full-reference methods have great limitations due to reference images are usually unavailable or nonexistent in practical applications. Therefore, it is necessary to introduce no-reference image quality assessment methods for HDR images. The rest of this paper is as follows: We describe in details the implementation process of the algorithm proposed in Section III, including the technique to detect saliency regions using SDSR[12] and the network structure designed of the visual quality perceptual model. We evaluate and compare the performance of different quality assessment algorithms in Section IV and draw conclusions in Section V. III. The Proposed Algorithm A brief overview of our algorithm of IQA for HDR is shown in Fig.1, where the proposed method is mainly composed of saliency region detection and the visual quality perception model that includes Error estimation network (Error-Net), Perceptual resistance network (PR-Net) and mixing function. Both the Error-Net and the PR-Net are CNN structures, which are used to calculate the error δ(i, j) and the visual perception resistance T(i, j) of the salient image block, respectively. The mixing function takes the output of these two networks as input and outputs the Differential mean opinion score (DMOS) of the saliency image block. The DMOS is a value proportional to the degree of distortion of the HDR image and represents the quality score of the image to be tested. Fig. 1Open in figure viewerPowerPoint Block diagram of proposed HDR-IQA algorithm 1. Saliency detection algorithm In this part, a method of salient region detection, which refers to the saliency detection by self-resemblance (SDSR) algorithm, is designed as the preprocessing link of the quality prediction model. Here, we fully take into account the impact of the HVS attention mechanism on quality assessment. In order to formalize the salience of each pixel, the saliency of the pixel at position Xi = [X1; X2]Ti is represented by a binary random variable yi as in Eq.(1). If Xi is significant, then yi is 1, otherwise it is 0. Then, the salient at the pixel position Xi can be denoted as the posterior probability Pr (yi = 1 |F), as in Eq.(2). y i = 1 , if X i is salient 0 , otherwise (1) where xi represents the pixel position, and yi represents whether the pixel at the position xi is salient. S i = Pr y i = 1 ∣ F (2) The feature matrix F = [f1i, …, fLi] denotes that th e central feature pixel Xi contains a set of feature vectors fi in a local neighborhood, where L is the number of features in the neighborhood. When L = 1, only a single feature vector is used. The feature set F = [F1, …, FN] is a matrix, which contains not only the features from the center, but also the number of feature matrices in a surrounding area. Using Bayes' theorem, Eq.(2) can be written as: S i = Pr y i = 1 ∣ F = p r F ∣ y i = 1 Pr y i = 1 p ( F ) (3) where Eq.(3) is based on the premise that each pixel is considered to be equally salient, hence salient can be denoted as the conditional probability density p(F |yi = 1). Fig. 2Open in figure viewerPowerPoint The flow of Saliency region detection From Fig.2, we can learn the implementation process of the saliency algorithm in this paper. Firstly, the SDSR is used to generate the saliency map. Then, get the saliency patches by rescaling. Finally, obtain salient image patches that we desired by calculating as Eq.(4). The saliency value S of each image patch is in the range of 0 – 1. We use PIi to denote its salience for the i – th image patch IPi, and compute its saliency value PIi as: P I i = ∑ w w = W - 1 ∑ h h = H - 1 S ( w , h ) (4) where S(w, h) is the saliency value at (w, h) in SPi. PIi is in the range of [0, w × h]. In this paper, a saliency threshold coefficient is introduced into saliency region detection algorithm. The saliency image patches that satisfy this equation: PIi ≥ α × W × H are selected for training. In our experiments, the value of α is {0, 0.1, 0.2, 0.3, 0.4, 0.5}. The higher the α is, the smaller a region is selected, which means the area is more important. When α = 0, the whole image is trained, is equivalent to not considering the HVS attention mechanism. 2. Visual quality perception model The visual quality perception model of this paper is described as the result of two CNN subnetworks and a mixing function. The role of the Error-Net is to obtain the error model while PR-Net is to perceptually scale this error, so as to calculate the HDR image quality score using the mixing function. After plenty of experiments we did, the parameter network of Error-Net and PR-Net was determined as shown in Table 1. 1) Error estimation network The Error-Net is a typical con volutional neural network structure that composed of convolution layer, pooling layer and fully connected layer. The input is a 32 × 32 image block. The last layer consists of a node corresponding to the output. ReLU is selected as the activation function of each convolutional layer. And a filter size of 2 is used for the maximum pooling after each convolution operation. In order to make the model get better characteristics, we set the size of the convolution kernel to decrease with increasing depth. Dropout functions are introduced after each convolution operation for preventing overfitting, and the ratio of Dropout increases with the depth of the layer in units of 0.1, with a maximum of 0.5. The output error δ(i, j) is the quantization of the changes of statistical information in the distorted image block. For the center (i, j) of an image block, the error is defined as: δ ( i , j ) = mean Y R ( i , j ) - Y D ( i , j ) (5) where YR an d YD are the luminance values at point (i, j) of the image blocks corresponding to the original HDR image and distorted HDR image, respectively. 2) Perceptual resistance network Different from the traditional perceptual modeling method for obtaining the perceptual threshold from psychophysical experiments, we use a data-driven method to calculate the perceptual resistance automatically by the model of PR-Net. Its aim is to express the overall response of all perception results displayed by the image block centered on (i, j). The PR-Net can be viewed as a function of mapping pixel values to perceptual resistance values. T(i, j) represents the perceptual resistance of each image block at point (i, j), and its value describes the difficulty degree of viewers to perceive the error δ(i, j). The higher the perceptual resistance, the quality of the image block is less affected and the better its quality is, and vice versa. For PR-Net, a CNN-based input optimization layer was added. In this layer, in addition to the luminance value of the image block, the mean, variance, and Mean Subtracted Contrast Normalized (MSCN) are calculated. The input is a 32 × 32 image block, and the output is the corresponding four feature maps. There are two convolutional layers and two fully connected layers after the optimization layer, and the last layer consists of a node corresponding to the output. Among them, the size of the convolution kernel of each convolution layer is 3 × 3, and the activation function is ReLU. Moreover, in order to prevent overfitting, dropout functions are introduced after each layer of the network. 3) Mixing function The purpose of the mixing function f ( δ ^ , T ) is to mix error δ(i, j) and perception resistance T(i, j), to generate a quality score. It is also a key part of our quality assessment system. Mixing function and PR-Net belong to the same process during training (see Fig.3). Therefore, the output of PR-Net will vary with the design of the mixing function f ( δ ^ , T ) . The mixing function of this system is denoted as follows: D M O S = f ( δ ^ , T ) = G δ ^ T (6) where G is a function that, in theory, only needs to satisfy monotonically increasing. However, if you choose an overly complicated G, the optimization problems will be introduced, and will lead to slow learning or zero learning due to the data points of the function are unstable. Based on these considerations and referred to Ref. [13], the G function we used is defined as G(x) = 1 – exp(–|kx|). The Eq.(6) can be written as follows: D M O S = 1 - exp - k × δ ^ ( i , j ) T ( i , j ) (7) In Eq.(7), a scale factor k was introduced. The k can be adjusted during the training, so as to predict the value of DMOS as close as possible to the subjective score. The DMOS score of these image blocks obtained is converted into an overall score by mean calculation as the final predicted score. Table 1. Network parameters of visual quality perception Error-Net Layer&parm PR-Net Layer&parm Conv Conv2D(64,7,activation='relu')+MaxPooling2D (pool_size=(2,2)+SpatialDropout2D (0.1) Conv Conv2D (64,3, activation='relu')+SpatialDropout2D (0.2) Conv Conv2D (128,5,activation='relu') +MaxPooling2D (pool_size= (2,2) +SpatialDropout2D (0.2) Conv Conv2D (128,3, activation='relu') +SpatialDropout2D (0.3) Conv Conv2D (256,3, activation='relu') + MaxPooling2D (pool_size= (2, 2) Dense Flatten () + Dropout (0.5)Dense (100, activation='relu') +Dropout (0.5) Conv Conv2D (512,1, activation='relu') +SpatialDropout2D (0.3) Dense Dense (100, activation='relu') Dense Flatten () + Dense (1) +Dropout (0.5) Dense (1) 3. Model training To the best of our knowledge, the accuracy of the label value is crucial to the performance of the model based on CNN. For the visual quality perception model in this paper, we use the subjective score of the entire HDR image as the label value of the local saliency image block. The training of prediction model is divided into two stages as see in Fig.3. Stage 1: Error-Net is trained with distorted HDR image block as input and δ as output, the error is calculated by Eq.(5). PR-Net network doesn't work during this process. Stage 2: During this training, all weights of E-net are frozen by setting the learning rate to zero. And J is used as the loss function of the image block centered on (i, j), which can be represented as: J ( i , j ) = D M O S ( i , j ) - D M O S t (8) where the DMOS(i, j) is the output of mixing function. At this stage, the PR-Net network and mixing function are trained together. Our image block is a region obtained by the HVS salience detection, so its label value is highly consistent with the overall image quality. Therefore, the method proposed in this paper is relatively accurate and reliable to evaluate the quality of the entire image. Fig. 3Open in figure viewerPowerPoint Model training procedure. (a) Stage 1; (b) Stage 2 IV. Experimental Results and Analysis CNN can ensure the accuracy and generalization ability of prediction model only when the training datasets are large and complete enough. Hence the dataset for usage needs to cover a variety of scenes and contain multiple types of distortion samples. Therefore, a new HDR image dataset, which is referred to as HDR-combineDB, was constructed by integrating existing publicly available HDR databases including Fairchild[14], HDR-JPEG[15] and HDR-compressionDB[16]. These public datasets are composed of 1145 HDR images, covering indoor objects, natural scenery, urban buildings and other scenes, can be used to explore static HDR image processing algorithms. In order to measure the accuracy and effectiveness of this algorithm, we use SROCC (Spearman rank-order correlation coefficient), PLCC (Pearson product-moment correlation coefficient) and RMSE (Root mean square error) to evaluate and compare. Where SROCC is the monotonicity assessment metric and PLCC is the accurate assessment metric of the prediction model. Both of them are in range of 0 – 1. The predicted result will achieve better performance if the value of SROCC and PLCC are closer to 1. RMSE is the consistency assessment metric of the prediction model and is in range of 0–100. The error of the assessment method will decline and the predicted result will achieve better performance if the value of RMSE becomes smaller. For the sake of verifying the effect of the visual attention mechanism on the prediction performance, HDR-combineDB was used as the training dataset in this experiment. We only change the value of saliency coefficient α, while remaining other parameters unchanged. The experimental results which are shown in Table 2 and statistical analysis in Fig.4 indicate that the performance of the algorithm in this paper increase first, and then decreases with the increase of α, which shows that the salience detection preprocessing based on HVS can improve the algorithm. In addition, When α is too high, the region of the saliency detection response will be too small, and the performance starts to degrade, which suggests that only when the salient region is selected in appropriate range can it achieve good predictive outcomes. Redundant information will derive from saliency area that is too large, and useful information will be lost when saliency area is too small. Table 2. Comparison of experiments using different coefficient α α SROCC PLCC RMSE α = 0 0.849 0.856 15.084 α = 0.1 0.904 0.890 13.104 α = 0.2 0.941 0.910 8.176 α = 0.3 0.850 0.863 20.049 α = 0.4 0.845 0.850 22.01 α = 0.5 0.765 0.710 25.56 Fig. 4Open in figure viewerPowerPoint Performance comparison under different α For the sake of verifying the generalization ability that the ability of the model to fit unknown samples in a cross-dataset environment. We set up two groups of experiments: one group is an independent experiment for each dataset and another group is a cross-dataset experiment. In the first group experience, all distorted HDR images of each dataset are randomly split into 80% for training, 20% for testing. In cross-dataset experiments, both Fairchild datasets and HDR-JPEG datasets were used for training and tested on the HDR-compressionDB dataset. In addition, the DOMS measurement is in range of 0 – 100 and the saliency threshold coefficient α is 0.2 during the experiment. The scatter plot shown in Fig.5, indicates that the new dataset HDR-combineDB constructed in this paper has the strongest linear fit in our experiment. Its performance is better than other datasets, which indicates that the integrity of distortion type and the scale of the dataset can affect the CNN learning effect to a certain extent. Fig.6, suggests that although the performance is not as good as those independent experiments of the first group, the overall trend is linear fitting, which means that objective value and subjective value have high correlation and better generalization ability. Fig. 5Open in figure viewerPowerPoint Experimental results trained by different datasets The model trained is used to evaluate the four types of HDR images randomly selected as to further illustrate the performance advantages of the algorithm. As shown in Fig.7, although there is a slight deviation between individual predicted values and label values, it can be seen that the label values are very close to the predicted values overall. Furthermore, as shown in Table 3, the performance of this algorithm is comparable to the most advanced full-reference HDR IQA algorithms HDR-VDP-2. 2, HDR-VQM and PU-SSIM. Table 3. Performance comparison of different HDR quality evaluation algorithms Method Proposed HDR-VDP-2.2 HDR-VQM PU-SSIM SROCC 0.941 0.930 0.919 0.912 PLCC 0.839 0.871 0.894 0.757 RMSE 8.176 10.127 10.87 12.947 Fig. 6Open in figure viewerPowerPoint Experimental results trained by cross datasets Fig. 7Open in figure viewerPowerPoint The prediction results of four distortion types V. Conclusions In this paper, the no-reference HDR IQA method we proposed, combines human visual characteristics and convolutional neural network. We first use self-similar saliency detection (SDSR) to extract the salient regions of the HDR image for mimicking the human visual attention mechanism from the perspective of subjective perception. Then, according to the visual characteristics of human perception of luminance and contrast sensitivity, a visual quality perception network based on CNN was designed for training quality prediction models. Finally, the objective quality score is calculated by the quality prediction model. Experimental results show that the method in this paper is highly consistent with subjective perception. The performance is competitive to existing classic FR-HDR IQA algorithms with the advantage of predicting multiple types of distortion HDR images without reference. Biographies FAN Kefeng (corresponding author) was born in December 1978, Ph.D., post-doctorate, researcher-level senior engineer. His research interests include intelligent information processing and cyber security. (E-mail: fankf@126.com) LIANG Jiyun was born in November 1994, Master of engineering. His research interests include video coding and image processing. (E-mail: 18589837505@163.com) LI Fei was born in July 1985, senior engineer. Her research interests include cloud computing and database, etc. (E-mail: fiona2022@163.com) QIU Puye was born in August 1994, Master of Science. His research interests include video coding and technical system management. (E-mail: qiupy@cesi.cn) References 1Artusi A, Richter T, Ebrahimi T, et al., "High dynamic range imaging technology", IEEE Signal Processing Magazine, 34(5): 165— 172, 2017. CrossrefWeb of Science®Google Scholar 2G. Yue, C. Hou and T. Zhou, "Blind quality assessment of tone-mapped images considering colorfulness, naturalness, and structure", in IEEE Transactions on Industrial Electronics, Vol. 66, No. 5, pp. 3784— 3793, 2019. CrossrefWeb of Science®Google Scholar 3M. Zhao, L. Shen, M. 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