Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360° video/image. First, this paper reviews both datasets and visual attention modelling approaches for 360° video/image. In this paper, we review the state-of-the-art works on 360° video/image processing from the aspects of perception, assessment and compression.
Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Nowadays, 360° video/image has been increasingly popular and drawn great attention. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The latter allows to derive fixation points from feature maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. This paper introduces a new framework to predict visual attention of omnidirectional images. Based on the assessment results, the tool decides how the final image will be rendered at the target device to produce a visually pleasing and high-quality image. We compare the texture mapping of a skybox with a procedural sphere and a cubemap using full-reference objective metrics for Image Quality Analysis (IQA). In this work, we propose a tool integrated within Unity editor to automate this quality assessment using different settings for the visualization of equirectangular images. Such parameters include resolution configurations, texture-to-objects mappings and deciding from different rendering approach, but to select the optimal value of these parameters, visual quality analysis is required. The development of a virtual reality viewer of panoramic images should consider several parameters that define the quality of the rendered image. Currently, the equirectangular image format is the widely used pattern to represent these panoramic images. Virtual Reality (VR) applications provide an immersive experience when using panoramic images that contain a 360-degree view of the scene.
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