4 research outputs found
IEEE Access special section editorial: Biologically inspired image processing challenges and future directions
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Panoramic video quality assessment based on non-local spherical CNN
© 1999-2012 IEEE. Panoramic video and stereoscopic panoramic video are essential carriers of virtual reality content, so it is very crucial to establish their quality assessment models for the standardization of virtual reality industry. However, it is very challenging to evaluate the quality of the panoramic video at present. One reason is that the spatial information of the panoramic video is warped due to the projection process, and the conventional video quality assessment (VQA) method is difficult to deal with this problem. Another reason is that the traditional VQA method is problematic to capture the complex global time information in the panoramic video. In response to the above questions, this paper presents an end-to-end neural network model to evaluate the quality of panoramic video and stereoscopic panoramic video. Compared to other panoramic video quality assessment methods, our proposed method combines spherical convolutional neural networks (CNN) and non-local neural networks, which can effectively extract complex spatiotemporal information of the panoramic video. We evaluate the method in two databases, VRQ-TJU and VR-VQA48. Experiments show the effectiveness of different modules in our method, and our method outperforms state-of-the-art other related methods
No-reference quality assessment of stereoscopic videos with inter-frame cross on a content-rich database
© 1991-2012 IEEE. 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
No reference quality assessment for screen content images using stacked auto-encoders in pictorial and textual regions
Recently, the visual quality evaluation of screen
content images (SCIs) has become an important and timely
emerging research theme. This paper presents an effective and
novel blind quality evaluation metric for SCIs by using stacked
auto-encoders (SAE) based on pictorial and textual regions. Since
the SCI consists of not only the pictorial area but also the
textual area, the human visual system (HVS) is not equally
sensitive to their different distortion types. Firstly, the textual
and pictorial regions can be obtained by dividing an input SCI
via a SCI segmentation metric. Next, we extract quality-aware
features from the textual region and pictorial region, respectively.
Then, two different SAEs are trained via an unsupervised
approach for quality-aware features which are extracted from
these two regions. After the training procedure of the SAEs, the
quality-aware features can evolve into more discriminative and
meaningful features. Subsequently, the evolved features and their
corresponding subjective scores are input into two regressors
for training. Each regressor can obtain one output predictive
score. Finally, the final perceptual quality score of a test SCI is
computed by these two predicted scores via a weighted model.
Experimental results on two public SCI-oriented databases have
revealed that the proposed scheme can compare favorably with
the existing blind image quality assessment metrics