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Related Experiment Video

Updated: Feb 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Quality Assessment of Screen Content Images via Convolutional-Neural-Network-Based Synthetic/Natural Segmentation.

Yi Zhang, Damon M Chandler, Xuanqin Mou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2018
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    Summary

    A new algorithm, convolutional neural network (CNN) based screen content image quality estimator (CNN-SQE), assesses image quality for screen content. This automated quality assessment (QA) method shows competitive performance against existing algorithms.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • The rise of remote desktop software and live streaming necessitates quality assessment for screen content images, which blend text, graphics, and photos.
    • Existing quality assessment (QA) research primarily focuses on natural images, leaving screen content QA an emerging field.

    Purpose of the Study:

    • To introduce a novel algorithm, the convolutional neural network (CNN) based screen content image quality estimator (CNN-SQE), for automated quality assessment of screen content images.
    • To develop a robust QA method capable of handling the diverse content types found in screen images.

    Main Methods:

    • Utilized fuzzy classification to categorize screen content into plain-text, computer graphics/cartoons, and natural image regions.
    • Employed a CNN for image classification, an edge-structure-based model for quality degradation, and a region-size-adaptive strategy for quality fusion.

    Main Results:

    • The CNN-SQE algorithm demonstrated effective fuzzy classification of screen content image regions.
    • The proposed algorithm achieved performance comparable or superior to current state-of-the-art QA methods.

    Conclusions:

    • The CNN-SQE presents a significant advancement in automated quality assessment for screen content images.
    • This approach offers a viable solution for quality monitoring and optimization in applications involving screen content.