SimilarityMetricValue

Computes a similarity criterion between two images and outputs the corresponding metric value.

Access to parameter description

This algorithm computes a similarity between two images

$I_1$ and

$I_2$ .
The proposed comparison metrics are:

The Mean Square Error:
$MSE = \frac{1}{N}\sum_{i=1}^{N}(I_1(i)-I_2(i))^2$
The normalized crossed correlation:
$NCC=\frac{\sum_{i=1}^{N}(I_1(i)-\overline{I_1})\ast (I_2(i)-\overline{I_2})}{ \sqrt{\sum_{i=1}^{N}(I_1(i)-\overline{I_1})^2}\ast\sqrt{\sum_{i=1}^{N}(I_2(i)-\overline{I_2})^2}}$

Where

$N$ is the total pixel number of each image.

(a)

(b)

(c)

Figure 1. Comparison of similarity metrics after adding an artificial gaussian noise of standard deviation

$\sigma$ on an original image:
(a) the reference image to compare, (b)

$\sigma$ =25, MSE = 513, NCC = 0.95 , (c)

$\sigma$ =80, MSE = 3235, NCC = 0.74
Note : Both input images must have same size and type.

See also

Function Syntax

This function returns outputMeasurement.

// Function prototype
SimilarityMetricValueMsr::Ptr
similarityMetricValue( std::shared_ptr< iolink::ImageView > inputImage1,
                       std::shared_ptr< iolink::ImageView > inputImage2,
                       SimilarityMetricValue::MetricType metricType,
                       SimilarityMetricValueMsr::Ptr outputMeasurement = nullptr );

This function returns outputMeasurement.

// Function prototype.
similarity_metric_value(input_image1: idt.ImageType,
                        input_image2: idt.ImageType,
                        metric_type: SimilarityMetricValue.MetricType = SimilarityMetricValue.MetricType.MEAN_SQUARE_ERROR,
                        output_measurement: Union[Any, None] = None) -> SimilarityMetricValueMsr

This function returns outputMeasurement.

// Function prototype.
public static SimilarityMetricValueMsr
SimilarityMetricValue( IOLink.ImageView inputImage1,
                       IOLink.ImageView inputImage2,
                       SimilarityMetricValue.MetricType metricType = ImageDev.SimilarityMetricValue.MetricType.MEAN_SQUARE_ERROR,
                       SimilarityMetricValueMsr outputMeasurement = null );

Class Syntax

// Command constructor.
SimilarityMetricValue();

/// Gets the inputImage1 parameter.
/// The first input image to compare.
std::shared_ptr< iolink::ImageView > inputImage1() const;
/// Sets the inputImage1 parameter.
/// The first input image to compare.
void setInputImage1( std::shared_ptr< iolink::ImageView > inputImage1 );

/// Gets the inputImage2 parameter.
/// The second input image to compare. Its dimensions and type must be the same as the first input.
std::shared_ptr< iolink::ImageView > inputImage2() const;
/// Sets the inputImage2 parameter.
/// The second input image to compare. Its dimensions and type must be the same as the first input.
void setInputImage2( std::shared_ptr< iolink::ImageView > inputImage2 );

/// Gets the metricType parameter.
/// The metric used to compare both input images
SimilarityMetricValue::MetricType metricType() const;
/// Sets the metricType parameter.
/// The metric used to compare both input images
void setMetricType( const SimilarityMetricValue::MetricType& metricType );

/// Gets the outputMeasurement parameter.
/// The output measurement result containing the similarity value in accordance with the selected metric.
SimilarityMetricValueMsr::Ptr outputMeasurement() const;


// Method to launch the command.
void execute();

# Property of the inputImage1 parameter.
SimilarityMetricValue.input_image1
# Property of the inputImage2 parameter.
SimilarityMetricValue.input_image2
# Property of the metricType parameter.
SimilarityMetricValue.metric_type
# Property of the outputMeasurement parameter.
SimilarityMetricValue.output_measurement

// Method to launch the command.
execute()

// Command constructor.
SimilarityMetricValue()

// Property of the inputImage1 parameter.
SimilarityMetricValue.inputImage1
// Property of the inputImage2 parameter.
SimilarityMetricValue.inputImage2
// Property of the metricType parameter.
SimilarityMetricValue.metricType
// Property of the outputMeasurement parameter.
SimilarityMetricValue.outputMeasurement

// Method to launch the command.
Execute()

Parameters

Parameter Name

Description

Type

Supported Values

Default Value

inputImage1

The first input image to compare.

Image

Binary, Label or Grayscale

nullptr

inputImage2

The second input image to compare. Its dimensions and type must be the same as the first input.

Image

Binary, Label, Grayscale or Multispectral

nullptr

metricType

The metric used to compare both input images

MEAN_SQUARE_ERROR	The computed metric is the mean-squared error.This metric is greater than or equal to 0. A value close to 0 means that both input images are similar. A high value that the image are significantly different.
CORRELATION	The computed metric is the correlation between both images, normalized in mean and variance.This metric is between 0 and 1. A high value means that both input images are similar. A value close to 0 means that the images are significantly different.

Enumeration

MEAN_SQUARE_ERROR

outputMeasurement

The output measurement result containing the similarity value in accordance with the selected metric.

SimilarityMetricValueMsr

nullptr

Object Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );
auto foam_gaussian_noise = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam_gaussian_noise.vip" );

SimilarityMetricValue similarityMetricValueAlgo;
similarityMetricValueAlgo.setInputImage1( foam );
similarityMetricValueAlgo.setInputImage2( foam_gaussian_noise );
similarityMetricValueAlgo.setMetricType( SimilarityMetricValue::MetricType::MEAN_SQUARE_ERROR );
similarityMetricValueAlgo.execute();

std::cout << "metric: " << similarityMetricValueAlgo.outputMeasurement()->metric( 0 ) ;

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))
foam_gaussian_noise = imagedev.read_vip_image(imagedev_data.get_image_path("foam_gaussian_noise.vip"))

similarity_metric_value_algo = imagedev.SimilarityMetricValue()
similarity_metric_value_algo.input_image1 = foam
similarity_metric_value_algo.input_image2 = foam_gaussian_noise
similarity_metric_value_algo.metric_type = imagedev.SimilarityMetricValue.MEAN_SQUARE_ERROR
similarity_metric_value_algo.execute()

print("metric: ", str(similarity_metric_value_algo.output_measurement.metric(0)))

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );
ImageView foam_gaussian_noise = Data.ReadVipImage( @"Data/images/foam_gaussian_noise.vip" );

SimilarityMetricValue similarityMetricValueAlgo = new SimilarityMetricValue
{
    inputImage1 = foam,
    inputImage2 = foam_gaussian_noise,
    metricType = SimilarityMetricValue.MetricType.MEAN_SQUARE_ERROR
};
similarityMetricValueAlgo.Execute();

Console.WriteLine( "metric: " + similarityMetricValueAlgo.outputMeasurement.metric( 0 ) );

Function Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );
auto foam_gaussian_noise = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam_gaussian_noise.vip" );

auto result = similarityMetricValue( foam, foam_gaussian_noise, SimilarityMetricValue::MetricType::MEAN_SQUARE_ERROR );

std::cout << "metric: " << result->metric( 0 ) ;

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))
foam_gaussian_noise = imagedev.read_vip_image(imagedev_data.get_image_path("foam_gaussian_noise.vip"))

result = imagedev.similarity_metric_value(foam, foam_gaussian_noise, imagedev.SimilarityMetricValue.MEAN_SQUARE_ERROR)

print("metric: ", str(result.metric(0)))

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );
ImageView foam_gaussian_noise = Data.ReadVipImage( @"Data/images/foam_gaussian_noise.vip" );

SimilarityMetricValueMsr result = Processing.SimilarityMetricValue( foam, foam_gaussian_noise, SimilarityMetricValue.MetricType.MEAN_SQUARE_ERROR );

Console.WriteLine(  "metric: " + result.metric( 0 )  );

SimilarityMetricValue

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax