Three Dimensional Scalable Video Adaptation via User-End Perceptual Quality Assessment

2008 ◽  
Vol 54 (3) ◽  
pp. 719-727 ◽  
Author(s):  
Guangtao Zhai ◽  
Jianfei Cai ◽  
Weisi Lin ◽  
Xiaokang Yang ◽  
Wenjun Zhang
Author(s):  
Sebastian Brand ◽  
Michael Kögel ◽  
Frank Altmann ◽  
Ingrid DeWolf ◽  
Ahmad Khaled ◽  
...  

Abstract Through Silicon Via (TSV) is the most promising technology for vertical interconnection in novel three-dimensional chip architectures. Reliability and quality assessment necessary for process development and manufacturing require appropriate non-destructive testing techniques to detect cracks and delamination defects with sufficient penetration and imaging capabilities. The current paper presents the application of two acoustically based methods operating in the GHz-frequency band for the assessment of the integrity of TSV structures.


2021 ◽  
pp. 1-1
Author(s):  
Evelyn Muschter ◽  
Andreas Noll ◽  
Jinting Zhao ◽  
Rania Hassen ◽  
Matti Strese ◽  
...  

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).


Author(s):  
Akhil Mulloth ◽  
Gabriel Banks ◽  
Giulio Zamboni ◽  
Simon Bather

Gas turbine performance is highly dependent on the quality of the manufactured parts. Manufacturing variations in the parts can significantly alter the performance, especially efficiency and thus SFC. The legacy process is to accept variations within predefined profile tolerance limits and a few other qualitative parameters, mostly at a few, key two-dimensional aerofoil sections. With the widespread use of White light scans and other similar three-dimensional scans, this has improved to include the three-dimensional profile. The future however may lie with performance based quality assessment of manufactured parts, combined with quantitative surface quality assessment to implement an intelligent screening process for the parts. The adjoint method, typically used for shape optimization is adapted to provide a prediction of the impact on performance due to manufacturing variations. The work presented outlines a three stage quality assessment process for manufactured parts, involving three-dimensional profile tolerance based screening, followed by a surface curvature based screening and finally an Adjoint based performance prediction.


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