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Titlebook: Digital Multimedia Communications; 20th International F Guangtao Zhai,Jun Zhou,Xiaokang Yang Conference proceedings 2024 The Editor(s) (if

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樓主: 浮華
11#
發(fā)表于 2025-3-23 10:44:34 | 只看該作者
Perceptual Blind Panoramic Image Quality Assessment Based on Super-Pixel, we fuse and map extracted features into quality scores by applying support vector regression (SVR). The experiments demonstrate the effectiveness and superiority of our proposed metric compared with state-of-the-art PIQA methods on the public panoramic image datasets.
12#
發(fā)表于 2025-3-23 15:20:38 | 只看該作者
Image Aesthetics Assessment Based on Visual Perception and Textual Semantic Understandinglti-modal combination features that contain multiple characteristics. Finally, the obtained multi-modal are combined for aesthetic assessment prediction. Experimental results on public image aesthetics assessment databases demonstrate the superiority of our model.
13#
發(fā)表于 2025-3-23 19:42:12 | 只看該作者
A No-Reference Stereoscopic Image Quality Assessment Based on?Cartoon Texture Decomposition and?Humanto the network for extracting relevant feature mappings. Finally, all sub-networks are used for quality scoring predictions, resulting in the final perceptual quality score. Experiments conducted on the LIVE dataset have demonstrated the superiority of this approach.
14#
發(fā)表于 2025-3-23 22:47:22 | 只看該作者
15#
發(fā)表于 2025-3-24 03:16:30 | 只看該作者
16#
發(fā)表于 2025-3-24 09:02:05 | 只看該作者
17#
發(fā)表于 2025-3-24 13:56:28 | 只看該作者
AUIQE: Attention-Based Underwater Image Quality Evaluator (AUIQE), a novel end-to-end IQA approach suitable for UIQA tasks. Specifically, we introduced channel and spatial dual attention mechanisms on the basis of the distortion characteristics of underwater images to make the network focus on some channels and spatial regions that are relevant to image q
18#
發(fā)表于 2025-3-24 16:49:11 | 只看該作者
19#
發(fā)表于 2025-3-24 22:56:10 | 只看該作者
An Omnidirectional Videos Quality Assessment Method Using Salient Object Informationppression algorithm based on salient objects (SO-NMS) to filter the recommended viewports. In the quality assessment stage, we utilizes a dense network to predict the quality scores of each viewport and then integrates these scores to obtain the video quality score. Experimental results demonstrate
20#
發(fā)表于 2025-3-25 01:30:39 | 只看該作者
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