3D Quality Evaluation Research Articles

3D Quality Evaluation of Rock Mass in Urban Underground Space Based on Improved Fuzzy Analytic Hierarchy Process

Urban underground space (UUS) is an important resource to address the shortage of land, save resources and reduce environmental pollution, but it is irreversible. Therefore, it is necessary to evaluate its suitability before development. Rock mass is not only the carrier of UUS utilization, but also a key evaluation index, as it directly affects the evaluation results. Therefore, it is necessary to evaluate the 3D quality of rock mass from the perspective of UUS development. In this paper, a 3D quality evaluation model of rock mass in UUS is established based on an improved fuzzy analytic hierarchy process (IFAHP). Firstly, the subjective weight is calculated by analytic hierarchy process (AHP), and the objective weight is calculated by super-standard multiple method (SSMM). Then a new weight combination method (grey correlation analysis) is adopted. This method organically combines the subjective and objective weights, and the results show that the combined weight is more reasonable. Then the combination weight and fuzzy comprehensive evaluation method are combined with MapGIS to establish a 3D evaluation model. The case analysis shows that the evaluation results of the national standard BQ method are conservative, the traditional fuzzy analytic hierarchy process (TFAHP) is smaller, and the IFAHP is more robust. The case analysis also shows that the 3D evaluation has the advantages of strong intuition, good visualization and high precision, which is better than the traditional 2D evaluation. Moreover, the 3D evaluation model can not only be applied to this case, but also to other projects, and can provide decision-making for the development of UUS.

A 3D Visual Security (3DVS) score to measure the visual security level of selectively encrypted 3D objects

Today, 3D objects are becoming more commonly used across many domains. However, it is necessary to secure them during their transmission over networks or when archiving them in the cloud. Encryption is a smart solution to protect 3D objects while remaining format compliant. While there exist many methods proposed for 3D quality evaluation, very few have been developed to evaluate the visual security level of encrypted 3D objects. In this paper, we propose an efficient metric, called 3D Visual Security (3DVS) score, to evaluate the visual security level of selectively encrypted 3D objects. First we present a new dataset composed of selectively encrypted 3D objects that have all been evaluated by more than 50 observers in terms of visual security. Secondly, we propose a model to determine the security parameters according to a desired security level. Finally, we detail our proposed 3DVS score which is based on full reference 3D metrics and serves to measure the visual security level of selectively encrypted 3D objects.

No-Reference Quality Assessment of Stereoscopic Videos With Inter-Frame Cross on a Content-Rich Database

With the wide application of stereoscopic video technology, the quality of stereoscopic video has attracted people’s attention. Objective stereoscopic video quality assessment (SVQA) is highly challenging, but essential, particularly the no-reference (NR) SVQA method, where reference information is not needed and a large number of samples are required for training and testing sets. However, as far as we know, there are only a few samples in the established stereo video database, which is unsuitable for NR quality assessment and seriously hampers the development of NR-SVQA method. For these difficulties that we encountered, we carry out a comprehensive subjective evaluation of stereoscopic video quality in our newly established TJU-SVQA databases that contain various contents, mixed resolution coding and symmetrically/asymmetrically distorted stereoscopic videos. Furthermore, we propose a new inter-frame cross map to predict the objective quality scores. We compare and analyze the performance of several state-of-the-art 2D and 3D quality evaluation methods on our new databases. The experimental results on our established databases and a public database demonstrate that the proposed method can robustly predict the quality of stereoscopic videos.

242. A new approach for MRI 3D image quality control: Protocol set up for multicoil arrays

Purpose The use of RF multicoil arrays for MR imaging has demonstrated several advantages, however when the number of coil elements increases, both signal and noise can become very inhomogeneous over the scanned volume, so standard 2D evaluations, as proposed by many protocols, are no longer representative of the whole volume and a new approach has to be considered. In this work we set up an automatic 3D quality evaluation particularly suited for head coils. Methods Measurements were performed on a Siemens Skyra 3T system with a 20 channel TX head coil, scanning an homogeneous spherical phantom with a standard clinical 3D protocol. Reconstruction was performed both for single sub-coil (uncombined images) and for the final image volume, that is a combination of all the sub-coils signals. Processing was performed offline with a bespoke imagej plugin. Main processing steps for combined volume are the automatic centring of a spherical region of interest (ROI) covering 80% of the phantom image volume, and the calculation of Signal to Noise Ratio (SNR), Normalized Absolute Average Difference (NAAD) and 5-color level reconstructed image with the NEMA algorithms for 2D images that were extended to 3D [1] . For uncombined sub-coil images, SNR is calculated in a 2 cm diameter sphere ROI automatically centered on the maximum average signal region, as showed in Fig. 1, and uniformity is evaluated with a 11-color levels volume reconstruction. Results Results for volumetric analysis are compared to the evaluation performed on 2D images from standard planes centered at the phantom isocenter. Results are very different particularly for uncombined analysis where the single coil is far from the evaluation slice, as reported for example in Table 1 for the SNR of coil n.2. In this case parameter variations due to malfunctioning are less sensitive with a 2D analysis. Conclusions We set up an automatic protocol for 3D quality evaluation which is more representative of the whole coil specification volume and more sensitive to failures when a single coil analysis is performed.