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Document image quality assessment (DIQA) aims to predict the visual quality of degraded document images. Although the definition of “visual quality” can change based on the specific applications, in this paper, we use OCR accuracy as a metric for quality and develop a novel no-reference DIQA method based on high order image statistics for OCR accuracy prediction. The proposed method consists of three...
In this work we describe a compact multi-task Convolutional Neural Network (CNN) for simultaneously estimating image quality and identifying distortions. CNNs are natural choices for multi-task problems because learned convolutional features may be shared by different high level tasks. However, we empirically argue that simply appending additional tasks based on the state of the art structure (e.g...
In this paper, we propose a novel “Opinion Free” (OF) No-Reference Video Quality Assessment (NR-VQA) algorithm based on frame-level unsupervised feature learning and hysteresis temporal pooling. The system consists of three components: feature extraction with max-min pooling, frame quality prediction and temporal pooling. Frame level features are first extracted by unsupervised feature learning and...
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