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Related Experiment Videos

SVD-based quality metric for image and video using machine learning.

Manish Narwaria1, Weisi Lin

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 639798. mani0018@ntu.edu.sg

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Mean Absolute Deviation01:13

Mean Absolute Deviation

The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...

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This study introduces a novel machine learning approach for visual quality evaluation using singular value decomposition (SVD) features. The data-driven method effectively pools features, outperforming existing techniques in image and video quality assessment.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Existing visual quality metrics often rely on ad hoc feature pooling techniques.
  • There is a need for more systematic and data-driven approaches to visual quality assessment.

Purpose of the Study:

  • To develop and evaluate a machine learning-based method for visual quality assessment.
  • To leverage Singular Value Decomposition (SVD) for robust visual feature extraction.
  • To address limitations of traditional feature pooling methods.

Main Methods:

  • Utilized Singular Value Decomposition (SVD) to extract singular values and vectors as visual features.
  • Employed machine learning for the feature pooling process, offering a data-driven alternative to traditional methods.

Related Experiment Videos

  • Conducted experiments on ten publicly available databases comprising 4042 images and 228 videos.

    • Main Results:

    • The proposed machine learning-based SVD feature pooling method demonstrated superior performance compared to eight existing visual quality assessment schemes.
    • Extensive analysis and cross-validation confirmed the robustness and effectiveness of the approach.
    • The method achieved state-of-the-art results in visual quality evaluation.

    Conclusions:

    • Machine learning offers a systematic and data-driven solution for feature pooling in visual quality assessment.
    • The proposed SVD-based feature extraction and machine learning pooling method is highly effective for evaluating image and video quality.
    • The study advocates for the adoption of machine learning in visual quality assessment for improved accuracy and reliability.