Deep salient wood image-based quality assessment

Research Article Deep salient wood image‑based quality assessment Risnandar1,2,3 · Esa Prakasa2,3 · Iwan Muhammad Erwin2,3 · Elli Ahmad Gojali2,3 · Herlan2,3 · Puji Lestari2,3 Received: 4 September 2019 / Accepted: 2 April 2020 © Springer Nature Switzerland AG 2020 Abstract We introduce a novelty in the method of the deep salient wood image quality assessment (DS-WIQA) for no-reference image quality assessment (NR-IQA). We exploit a five-layer deep convolutional neural network (DCNN) for the salient wood image map. DS-WIQA also employs the n-convex-concave model. The outcomes obviously prove that our DCNN and DSWIQA architectures can deliver a superior achievement on Zenodo and Lignoindo datasets, respectively. We compute a salient wood image map of each wood image in small wood image patches. Our exploratory outcomes evince that the proposed DCNN and DS-WIQA methods are superior to other the advanced methods on Zenodo and Lignoindo datasets, respectively. Our proposed DCNN for NR-IQA also obtains a better result compared with the other NR-IQA methods in the five distortion types of JP2K, JPEG, white noise Gaussian, blocking artifact, and the fast fading and also in the undistorted wood images. Our DCNN outruns the recent most sophisticated methods in terms of SROCC and LCC evaluation, respectively. DS-WIQA outpaces other the advanced methods by 0.38% and 0.22% greater than our proposed DCNN, and 34.84% and 30.15% greater than other methods with respect to SROCC and LCC, respectively. In computational complexity of our proposed DCNN and DS-WIQA cut down the shift add operation in exponential, logarithmic, and trigonometric functions. DS-WIQA shows up to be more significant than our proposed DCNN and the other DCNN methods. Keywords NR-IQA · DCNN · DS-WIQA · Salient wood image map · Convex · Concave 1 Introduction Wood species recognition is still a new discovery in the computer vision which has a challenging task for the welltrained experts to study the characteristic on the wood surfaces under the macroscopic and microscopic views. The wood image quality intensely depends on the wood capturing quality. Many objective image quality assessment (IQA) methods propose to codify image quality. If we use a full-reference image quality assessment (FR-IQA), the observer can better assess the image by considering the distorted and undistorted image. We assess the wood image quality from no-reference wood image. In the study of [1], they observed two types of IQA methods. They use the distorted image which causes Gaussian white noise or Gaussian blur and also human visual system (HVS) method. We propose the problem solving of that two points by combining deep convolutional neural networks (DCNNs) as a sophisticated method with saliency map. The IQA methods were mentioned in the distortion type. The study of [2] offered a NR-IQA method for JPEG2000 compression by associating a couple Gaussian mixture and wavelet coefficient. The most recent study observes the more distortion type and also the unknown distortion type. NR-IQA methods can be restricted into natural scene statistics (NSS) and the training-based methods. In NSS, * Risnandar, ; ; ; Esa Prakasa, ; Iwan Muhammad Erwin, ; Elli Ahmad Gojali, ; Herlan, ; Puji Lestari, | 1Intelligent, Computing, and Multimedia (ICM) Research Group, School of Computing/Dept. of Informatics, Faculty of Informatics, Telkom University, Bandung, Indonesia. 2Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences (LIPI), Bandung, Indonesia. 3Ministry of Research and Technology/National Agency for Research and Innovation (BRIN), Jakarta, Republic of Indonesia. SN Applied Sciences (2020) 2:1034 | https://doi.org/10.1007/s42452-020-2671-x Vol.:(0123456789) Research Article SN Applied Sciences (2020) 2:1034 the distorted image can be detected in the undistorted image as mentioned in [2–6]. In the training-based method, we study the features learned from images where the classifier is trained. The training-based method can be recognized as the conventional machine learning assessment [7–11]. The conventional CNN method extracts the image features in recognition and training-based of the IQA. CNN technique concerns in the object classification [12], age and gender recognition [13], or fashion recognition [14]. Many CNN outcomes have been established to NR-IQA and accomplished the advanced outcomes [7–9] and also in the feature derivation [12, 13, 15, 16]. We propose a deeper CNN, which combines with salient wood image map, namely, deep salient wood imagebased quality assessment (DS-WIQA). DS-WIQA architecture has five convolutional layers in our proposed DCNN, which is deeper than AlexNet which has three convolutional layers [17]. Compared with a closely DCNN, AlexNet [17] does not fit the deeper training model. DS-WIQA uses the convex and concave n-square methods for the salient wood image map. The saliency map of CNN-based in [1] did not analyze HVS into IQA. While in the works of [7, 8], all of images can be extracted to many image patches. In Fig. 3, HVS codifies the wood images quality. Unfortunately, it is difficult to perceive the difference between one and the other wood image patches. It causes a low quality assessment to all patches within a wood image. To introduce HVS, DS-WIQA combines the proposed DCNN which has the convex and concave n-square methods for the salient wood image map method. The more closer of our studies [9, 18] exploit a gradient map as wood’s image patch court. The others, [19, 20] calculates saliency map for each image and its saliency patch score of each patch. We experimented with a proposed DCNN algorithm on Zenodo wood species [21] and Lignoindo [22] datasets. Our experiment employs Spearman’s rank order correlation coefficient (SROCC) and the linear correlation coefficient (LCC) scores, respectively. Our outcome shows that our salient wood image maps can improve DCNN in NRIQA. To validate the work of our DS-WIQA, we also analyze a DS-WIQA model on the Zenodo dataset and spread it on the Lignoindo dataset for cross-dataset evaluation. Our DS-WIQA obtains the advanced outcomes on Zenodo and Lignoindo datasets. 2 Related work The combining NSS-based NR-IQA method explores to capture statistical properties of the undistorted wood images. To evaluate the NSS performance, most algorithms formulate the distributions or train a model. In the study of [6], the NSS is evaluated on a set of wavelet Vol:.(1234567890) | https://doi.org/10.1007/s42452-020-2671-x coefficients. They identify the distortion image before applying a distortion classifier. The other case, method in [2] transforms each image using discrete cosine transform (DCT) and the resulting coefficients are used for a generalized Gaussian density model. Method in [4] extracts features in the shearlet domain by using a NRIQA and neural network method. However, the autoencoder used in that method is d (...truncated)