The current researches on 3D point cloud quality evaluation focus on the full reference methods, but the blind quality evaluation has more advantages in actual applications. Therefore, a Blind Quality Evaluator for Colored Point Cloud (CPC) based on Visual Perception (BQE-CVP) is proposed. Firstly, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined. Secondly, considering the visual masking effect of CPC's color information, the CPC is projected to the plane and the corresponding visual perception features are extracted. Then, the CPC's color combination feature vector is extracted in the 3D domain to better reflect the color distortion. Thirdly, the joint feature of CPC is extracted as the supplement of the geometric and color features. Finally, all the extracted features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (CPCD2.0, IRPC and SJTU-PCQA) show that the proposed metric is better than relevant existing methods.

BQE-CVP: Blind Quality Evaluator for Colored Point Cloud Based on Visual Perception

From the perspective of optimizing the visual experience of the omnidirectional system, this article explores the terminal image quality of the high dynamic range omnidirectional image (HOI) system for the first time. The HOI system has higher contrast and wider field of view perception, but its complex imaging process poses a challenge to quality measurement. In this article, we propose a novel quality measurement method for HOI (QM-HOI) system based on distortion perception and brain information-processing mechanism. Because HOI system may encounter single distortion or mixed distortion, the proposed method consists of two modules, i.e., local progressive distortion model (LPDM) and global mixed distortion model (GMDM). LPDM is designed to mainly extract local features based on the different perception effects that tone mapping (TM) operators impart to coding distortion in different luminance regions. GMDM is designed to jointly consider the structure distortion caused by coding and the color distortion of TM. The extracted features are formed into feature vectors to predict the visual quality of the distorted HOIs. A new HOI subjective evaluation dataset with multidistortion is established, called NBU-HOID, specialized for the design and testing of the proposed method. Experimental results show that the objective measurement results of the proposed method is better than the state-of-the-art methods, and more in line with the human perception. We will also provide the NBU-HOID dataset free of charge shortly afterward.

Quality Measurement for High Dynamic Range Omnidirectional Image Systems

PURPOSE The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone-beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images. METHODS In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi-plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K-means clustering algorithm is used to cluster the texture parameters of the gray level-gradient co-occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line-tracking method, and smoothed by the fourth-order polynomial to obtain the final segmentation result. RESULTS Twenty-one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively. CONCLUSIONS The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented.

Inferior alveolar canal segmentation based on cone-beam computed tomography.

Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distribution, and fitting parameters of asymmetrical generalized Gaussian distribution are extracted. Thirdly, the features extracted in the dual-domain are fused to form the quality-aware feature vector. Finally, quality regression process by random forest is conducted to build the relationship between image features and quality score, yielding a measure of image quality. The resulting algorithm is tested on the LIVE database and compared with competing IQA models. Experimental results on the LIVE database indicate that the proposed DFF-IQA method is more consistent with the human visual system than other competing IQA methods.

https://www.mdpi.com/1099-4300/22/3/344/pdf

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

One of the main open challenges in visualisation applications such as cathode ray tube (CRT) monitor, liquid-crystal display (LCD), and organic light-emitting diode (OLED) display is the robustness for high dynamic range (HDR) environs. This is due to the imperfections in the sensor and the incapability to track interest points successfully because of the brightness constancy in visualisation applications. To address this problem, different tone mapping operators are required for visualising HDR images on standard displays. However, these standard displays have different dynamic ranges. Thus, there is a need for a new model to find the best quality tone mapped image for specific kinds of visualisation applications. The authors propose a hybrid deep emperor penguin classifier to accurately classify the tone mapped images for different visualisation applications. Here, a selective deep neural network is trained to predict the quality of a tone-mapped image. Based on this quality, a decision is made as to the suitability of the image for CRT monitor, LCD display or OLED display. Also, they evaluate the proposed model on the TMIQD database and the simulation results prove that the proposed model outperforms the state-of-the-art image quality assessment methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.1371

Hybrid deep emperor penguin classifier algorithm-based image quality assessment for visualisation application in HDR environments

Blind Tone Mapped Image Quality Assessment with Image Segmentation and Visual Perception

The invention discloses a three-dimensional image visual comfort improvement method combined with perception depth quality, and the method comprises the steps: firstly building a visual satisfaction objective evaluation model which is used for guiding an improvement process, and enabling the model to fuse visual comfort and perception depth quality objective evaluation composed of a perception absolute distance and a perception relative distance; for the original three-dimensional image needing to improve the visual comfort, redrawing by adopting a viewing distance nonlinear translation method, wherein the redrawn three-dimensional image has the characteristic that the geometric proportion of a virtual scene is not changed; and respectively calculating visual satisfaction and visual comfort objective evaluation values of the redrawn three-dimensional image, wherein when the visual satisfaction reaches the maximum value in multiple times of redrawing and the visual comfort value is greater than the visual comfort value of the original three-dimensional image, the redrawn three-dimensional image is an optimal three-dimensional image, and the improved three-dimensional image still maintains better perception depth quality while improving the visual comfort, and is more consistent with the preference of a human visual system.

Three-dimensional image visual comfort improvement method combined with perception depth quality

The invention discloses an objective evaluation method for visual satisfaction of three-dimensional images The method comprises: obtaining an average subjective evaluation mean value of the 3D visualsatisfaction of each three-dimensional image in a training set during training; calculating a visual comfort objective evaluation value, a perception absolute distance objective evaluation value anda perception relative distance objective evaluation value of each three-dimensional image; fitting all the average subjective evaluation mean values, all the visual comfort objective evaluation values, all the perception absolute distance objective evaluation values and all the perception relative distance objective evaluation values by using a training model epsilon-SVR to obtain a three-dimensional image visual satisfaction objective evaluation model; and, during testing, obtaining a visual comfort objective evaluation value, a perception absolute distance objective evaluation value and a perception relative distance objective evaluation value of a to-be-evaluated three-dimensional image, inputting the values into the three-dimensional image visual satisfaction objective evaluation model, and predicting to obtain a visual satisfaction prediction value The method has the advantage that the objective evaluation result consistent with the subjective perception of the satisfaction degree of the human on the three-dimensional image can be predicted

Chi Biwei

Papers

Blind Tone Mapped Image Quality Assessment with Image Segmentation and Visual Perception

Three-dimensional image visual comfort improvement method combined with perception depth quality

Objective evaluation method for visual satisfaction of three-dimensional images