The rapid growth of third and development of future generation mobile systems has led to an increase in the demand for image and video services. However, the hostile nature of the wireless channel makes the deployment of such services much more challenging, as in the case of a wireline system. In this context, the importance of taking care of user satisfaction with service provisioning as a whole has been recognized. The related user-oriented quality concepts cover end-to-end quality of service and subjective factors such as experiences with the service. To monitor quality and adapt system resources, performance indicators that represent service integrity have to be selected and related to objective measures that correlate well with the quality as perceived by humans. Such objective perceptual quality metrics can then be utilized to optimize quality perception associated with applications in technical systems.In this paper, we focus on the design of reduced-reference objective perceptual image quality metrics for use in wireless imaging. Specifically, the Normalized Hybrid Image Quality Metric (NHIQM) and a perceptual relevance weighted -norm are designed. The main idea behind both feature-based metrics relates to the fact that the human visual system (HVS) is trained to extract structural information from the viewing area. Accordingly, NHIQM and -norm are designed to account for different structural artifacts that have been observed in our distortion model of a wireless link. The extent by which individual artifacts are present in a given image is obtained by measuring related image features. The overall quality measure is then computed as a weighting sum of the features with the respective perceptual relevance weight obtained from subjective experiments. The proposed metrics differ mainly in the pooling of the features and amount of reduced-reference produced. While NHIQM performs the pooling at the transmitter of the system to produce a single value as reduced-reference, the -norm requires all involved feature values from the transmitted and received image to perform the pooling on the feature differences at the receiver. In addition, non-linear mapping functions are developed that relate the metric values to predicted mean opinion scores (MOS) and account for saturations in the HVS. The evaluation of prediction performance of NHIQM and the -norm reveals their excellent correlation with human perception in terms of accuracy, monotonicity, and consistency. This holds not only for the prediction performance on images taken for the training of the metrics but also for the generalization to unknown images. In addition, it is shown that the NHIQM approach and the perceptual relevance weighted -norm outperform other prominent objective quality metrics in prediction performance.
Key words:Objective perceptual image quality, Normalized hybrid image quality metric, Perceptual relevance weighted -norm, Reduced-reference, Wireless imaging * Corresponding author.Email addresses: ulrich.engelke@bth.se (Ulrich Enge...
<p>This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.</p>
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