OpenCV/opencv_contrib/modules/quality

//! @addtogroup quality //! @{

Quality API, Image Quality Analysis

Implementation of various image quality analysis (IQA) algorithms

• Mean squared error (MSE) https://en.wikipedia.org/wiki/Mean_squared_error

• Peak signal-to-noise ratio (PSNR) https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

• Structural similarity (SSIM) https://en.wikipedia.org/wiki/Structural_similarity

• Gradient Magnitude Similarity Deviation (GMSD) http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm In general, the GMSD algorithm should yield the best result for full-reference IQA.

• Blind/Referenceless Image Spatial Quality Evaluation (BRISQUE) http://live.ece.utexas.edu/research/Quality/nrqa.htm

Interface/Usage

All algorithms can be accessed through the simpler static compute methods, or be accessed by instance created via the static create methods.

Instance methods are designed to be more performant when comparing one source file against multiple comparison files, as the algorithm-specific preprocessing on the source file need not be repeated with each call.

For performance reaasons, it is recommended, but not required, for users of this module to convert input images to grayscale images prior to processing. SSIM and GMSD were originally tested by their respective researchers on grayscale uint8 images, but this implementation will compute the values for each channel if the user desires to do so.

BRISQUE is a NR-IQA algorithm (No-Reference) which doesn't require a reference image.

Quick Start/Usage

C++ Implementations

For Full Reference IQA Algorithms (MSE, PSNR, SSIM, GMSD)

    #include <opencv2/quality.hpp>
    cv::Mat img1, img2; /* your cv::Mat images to compare */
    cv::Mat quality_map;  /* output quality map (optional) */
    /* compute MSE via static method */
    cv::Scalar result_static = quality::QualityMSE::compute(img1, img2, quality_map);  /* or cv::noArray() if not interested in output quality maps */
    /* alternatively, compute MSE via instance */
    cv::Ptr<quality::QualityBase> ptr = quality::QualityMSE::create(img1);
    cv::Scalar result = ptr->compute( img2 );  /* compute MSE, compare img1 vs img2 */
    ptr->getQualityMap(quality_map);  /* optionally, access output quality maps */

For No Reference IQA Algorithm (BRISQUE)

    #include <opencv2/quality.hpp>
    cv::Mat img = cv::imread("/path/to/my_image.bmp"); // path to the image to evaluate
    cv::String model_path = "path/to/brisque_model_live.yml"; // path to the trained model
    cv::String range_path = "path/to/brisque_range_live.yml"; // path to range file
    /* compute BRISQUE quality score via static method */
    cv::Scalar result_static = quality::QualityBRISQUE::compute(img,
model_path, range_path);
    /* alternatively, compute BRISQUE via instance */
    cv::Ptr<quality::QualityBase> ptr = quality::QualityBRISQUE::create(model_path, range_path);
    cv::Scalar result = ptr->compute(img); /* computes BRISQUE score for img */

Python Implementations

For Full Reference IQA Algorithms (MSE, PSNR, SSIM, GSMD)

    import cv2
    # read images
    img1 = cv2.imread(img1, 1) # specify img1
    img2 = cv2.imread(img2_path, 1) # specify img2_path
    # compute MSE score and quality maps via static method
    result_static, quality_map = cv2.quality.QualityMSE_compute(img1, img2)
    # compute MSE score and quality maps via Instance
    obj = cv2.quality.QualityMSE_create(img1)
    result = obj.compute(img2)
    quality_map = obj.getQualityMap()

For No Reference IQA Algorithm (BRISQUE)

    import cv2
    # read image
    img = cv2.imread(img_path, 1) # mention img_path
    # compute brisque quality score via static method
    score = cv2.quality.QualityBRISQUE_compute(img, model_path,
range_path) # specify model_path and range_path
    # compute brisque quality score via instance
    # specify model_path and range_path
    obj = cv2.quality.QualityBRISQUE_create(model_path, range_path)
    score = obj.compute(img)

Library Design

Each implemented algorithm shall:

• Inherit from QualityBase, and properly implement/override compute, empty and clear instance methods, along with a static compute method.
• Accept one cv::Mat or cv::UMat via InputArray for computation. Each input cv::Mat or cv::UMat may contain one or more channels. If the algorithm does not support multiple channels, it should be documented and an appropriate assertion should be in place.
• Return a cv::Scalar with per-channel computed value
• Compute result via a single, static method named compute and via an overridden instance method (see compute in qualitybase.hpp).
• Perform any setup and/or pre-processing of reference images in the constructor, allowing for efficient computation when comparing the reference image versus multiple comparison image(s). No-reference algorithms should accept images for evaluation in the compute method.
• Optionally compute resulting quality map. Instance compute method should store them in QualityBase::_qualityMap as the mat type defined by QualityBase::_mat_type, or override QualityBase::getQualityMap. Static compute method should return the quality map in an OutputArray parameter.
• Document algorithm in this readme and in its respective header. Documentation should include interpretation for the results of compute as well as the format of the output quality map (if supported), along with any other notable usage information.
• Implement tests of static compute method and instance methods using single- and multi-channel images and OpenCL enabled and disabled

To Do

• Document the output quality maps for each algorithm
• Investigate precision loss with cv::Filter2D + UMat + CV_32F + OCL for GMSD

//! @}