Quality assessment for virtual reality technology based on real scene

Virtual reality technology is a new display technology, which provides users with real viewing experience. As known, most of the virtual reality display through stereoscopic images. However, image quality will be influenced by the collection, storage and transmission process. If the stereoscopic image quality in the virtual reality technology is seriously damaged, the user will feel uncomfortable, and this can even cause healthy problems. In this paper, we establish a set of accurate and effective evaluations for the virtual reality. In the preprocessing, we segment the original reference and distorted image into binocular regions and monocular regions. Then, the Information-weighted SSIM (IW-SSIM) or Information-weighted PSNR (IW-PSNR) values over the monocular regions are applied to obtain the IW-score. At the same time, the Stereo-weighted-SSIM (SW-SSIM) or Stereo-weighted-PSNR (SW-PSNR) can be used to calculate the SW-score. Finally, we pool the stereoscopic images score by combing the IW-score and SW-score. Experiments show that our method is very consistent with human subjective judgment standard in the evaluation of virtual reality technology

Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process

In this paper, we propose a sparse representation based Reduced-Reference Image Quality Assessment (RR-IQA) index for stereoscopic images from the following two perspectives: 1) Human visual system (HVS) always tries to infer the meaningful information and reduces uncertainty from the visual stimuli, and the entropy of primitive (EoP) can well describe this visual cognitive progress when perceiving natural images. 2) Ocular dominance (also known as binocularity) which represents the interaction between two eyes is quantified by the sparse representation coefficients. Inspired by previous research, the perception and understanding of an image is considered as an active inference process determined by the level of “surprise”, which can be described by EoP. Therefore, the primitives learnt from natural images can be utilized to evaluate the visual information by computing entropy. Meanwhile, considering the binocularity in stereo image quality assessment, a feasible way is proposed to characterize this binocular process according to the sparse representation coefficients of each view. Experimental results on LIVE 3D image databases and MCL database further demonstrate that the proposed algorithm achieves high consistency with subjective evaluation

A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

A deep evaluator for image retargeting quality by geometrical and contextual interaction

An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results

No reference quality assessment of stereo video based on saliency and sparsity

With the popularity of video technology, stereoscopic video quality assessment (SVQA) has become increasingly important. Existing SVQA methods cannot achieve good performance because the videos' information is not fully utilized. In this paper, we consider various information in the videos together, construct a simple model to combine and analyze the diverse features, which is based on saliency and sparsity. First, we utilize the 3-D saliency map of sum map, which remains the basic information of stereoscopic video, as a valid tool to evaluate the videos' quality. Second, we use the sparse representation to decompose the sum map of 3-D saliency into coefficients, then calculate the features based on sparse coefficients to obtain the effective expression of videos' message. Next, in order to reduce the relevance between the features, we put them into stacked auto-encoder, mapping vectors to higher dimensional space, and adding the sparse restraint, then input them into support vector machine subsequently, and finally, get the quality assessment scores. Within that process, we take the advantage of saliency and sparsity to extract and simplify features. Through the later experiment, we can see the proposed method is fitting well with the subjective scores

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

Control of Mode/Bond Selectivity and Product Energy Disposal by the Transition State: X + H₂O (X = H, F, O(³P), and Cl) Reactions

The ability to predict mode/bond selectivity and energy disposal is of central importance for controlling chemical reactions. We argue that the transition state plays a critical role in state-to-state reactivity and propose a simple sudden model based on coupling with the reaction coordinate at the transition state. The applicability of this so-called sudden vector projection (SVP) model is examined for several prototypical atom–triatom, namely, X + H₂O (X = H, F, O­(³P), and Cl) reactions. It is shown that the SVP model is capable of qualitatively predicting experimental and full-dimensional quantum dynamical results, including those reported in this work, for these polyatomic reactions. These results, and those for other reactions, suggest that the SVP model offers a general paradigm for understanding quantum state resolved reactivity in bimolecular reactions

UV Absorption Spectrum and Photodissociation Channels of the Simplest Criegee Intermediate (CH₂OO)

The lowest-lying singlet states of the simplest Criegee intermediate (CH₂OO) have been characterized along the O–O dissociation coordinate using explicitly correlated MRCI-F12 electronic structure theory and large active spaces. It is found that a high-level treatment of dynamic electron-correlation is essential to accurately describe these states. A significant well on the <i>B</i>-state is identified at the MRCI-F12 level with an equilibrium structure that differs substantially from that of the ground <i>X</i>-state. This well is presumably responsible for the apparent vibrational structure in some experimental UV absorption spectra, analogous to the structured Huggins band of the iso-electronic ozone. The <i>B</i>-state potential in the Franck–Condon region is sufficiently accurate that an absorption spectrum calculated with a one-dimensional model agrees remarkably well with experiment

Reactant Vibrational Excitations Are More Effective than Translational Energy in Promoting an Early-Barrier Reaction F + H₂O → HF + OH

The exothermic F + H₂O → HF + OH reaction has a decidedly “early” or “reactant-like” barrier. According to a naïve interpretation of the Polanyi’s rules, translational energy would be more effective than vibrational energy in promoting such reactions. However, we demonstrate here using both quasi-classical trajectory and full-dimensional quantum wave packet methods on an accurate global potential energy surface that excitations in the H₂O vibrational degrees of freedom have higher efficacy in enhancing the reactivity of the title reaction than the same amount of translational energy, thus providing a counter-example to Polanyi’s rules. This enhancement of reactivity is analyzed using a vibrational adiabatic model, which sheds light on the surprising mode selectivity in this reaction

State-to-State Photodissociation Dynamics of H₂O in the B‑band: Competition between Two Coexisting Nonadiabatic Pathways

The photodissociation of H₂O in its B band is a prototype for nonadiabatic reaction dynamics. In addition to dissociation via the adiabatic pathway to the OH­(òΣ⁺) + H fragments, it also produces the OH­(X̃²Π) + H fragments through two nonadiabatic pathways: the B̃ → X̃ transition via two conical intersections and the B̃ → à transition via a Renner–Teller pair. In this work, the state-to-state dissociation dynamics in all three channels are investigated with a full-dimensional quantum mechanical model using a set of coupled diabatic potential energy surfaces determined at the internally contracted multireference configuration interaction level with the aug-cc-pVQZ basis set. The inclusion of all relevant electronic states not only results in an improved agreement with the latest experimental data but also sheds valuable insights into the competition between the two coexisting nonadiabatic pathways