The Research Of Virtual Reality Video Image Quality Assessment

With the development of multimedia technology,the hardware is constantly updated and perfected,and virtual reality(VR)technology has gradually entered people's vision.As an emerging media,it has attracted extensive attention and research interest.However,due to the high resolution of VR video images,it is difficult to transmit?compress?and store.At the same time,the implementation of VR requires specific wearing conditions,which limits the further development of the industry.Therefore,it is of great significance to study VR video image quality assessment methods to promote the development of the VR industry.However,for the image quality assessment of the VR video image,there is still a lack of an appropriate method.At present,the traditional image quality assessment method is still widely used.On the other hand,the lack of VR video image data sets is also a key factor in restricting VR video image quality assessment research.Therefore,this article aims to create VR video image data sets to alleviate this shortage,and study the subjective assessment and objective assessment methods of VR video images to promote the further development of VR technology-related fields.The main research results are as follows:In view of the shortage of VR video image data sets,this paper established two VR video image databases and proposed a subjective quality assessment method for VR video images,which alleviates the current lack of a dedicated VR video image subjective evaluation method and database problem.The experimental results prove that the proposed scheme for subjective quality assessment of VR video images can correctly evaluate the quality of VR images,and then verify the reliability of the two data sets established,which lays a good foundation for the study of objective quality assessment.An objective quality assessment model for VR video images based on 3D convolutional neural network is designed.This method extracts 10 frames of video equally,then merges them,and then divides them into 128 * 128 small blocks in a non-overlapping and non-interval manner as one of the input parameters of the network,and simultaneously inputs the subjective assessment score and the weight value of the video image into the network.Finally,after training and testing,an objective quality evaluation model of 3D convolutional neural network was established.This model is more suitable for video analysis because it retains input time information compared to 2D convolutional neural networks.The experimental results also prove the superiority of the model performance.An objective quality assessment method for VR images based on support vector machines is proposed.This method is based on the characteristics of the VR image.In addition to extracting the brightness,hue,saturation,and texture features of the image,the saliency analysis of the VR image is performed,and the saliency map of the VR image is used as one-dimensional feature.Then we use sparse dictionary learning to sparse the salient features,and finally use principal Component Analysis(PCA)to perform dimensionality reduction on the extracted features to obtain a joint representation feature as the final feature value.Finally,combining the joint eigenvalues and subjective scores,an objective quality assessment model based on support vector regression was established through training,and it was used to predict the quality of VR image data sets.The assessment method has better performance.