SsimValue3d
Computes the mean structural similarity (SSIM) index between two three-dimensional images.
Access to parameter description
This algorithm provides a measure of similarity by computing the SSIM value for each pixel of the couple of image and by returning the mean of all these values.
For each pixel, we compute the SSIM value by using the following formula :
$$ SSIM(I_{1},I_{2}) = \frac{\left ( 2\mu_{1} \mu_{2} +C_{1}\right )\left ( 2\sigma_{12}+C_{2}\right )}{\left ( \mu_{1}^{2}+ \mu_{2}^{2} +C_{1}\right )\left ( \sigma_{1}^{2}+\sigma_{2}^{2}+C_{2}\right )}$$
Where:
Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. "Image quality assessment: From error visibility to structural similarity". IEEE Transactions on Image Processing , vol. 13, pp. 600-612, Apr. 2004.
See also
Access to parameter description
This algorithm provides a measure of similarity by computing the SSIM value for each pixel of the couple of image and by returning the mean of all these values.
For each pixel, we compute the SSIM value by using the following formula :
$$ SSIM(I_{1},I_{2}) = \frac{\left ( 2\mu_{1} \mu_{2} +C_{1}\right )\left ( 2\sigma_{12}+C_{2}\right )}{\left ( \mu_{1}^{2}+ \mu_{2}^{2} +C_{1}\right )\left ( \sigma_{1}^{2}+\sigma_{2}^{2}+C_{2}\right )}$$
Where:
- $\mu_{1}$ is the mean in the tile arround the current pixel in the image $I_{1}$,
- $\mu_{2}$ is the mean in the tile arround the current pixel in the image $I_{2}$,
- $\sigma_{1}$ is the standard deviation in the tile arround the current pixel in the image $I_{1}$,
- $\sigma_{2}$ is the standard deviation in the tile arround the current pixel in the image $I_{2}$,
- $\sigma_{12}$ is the covariance in the tile arround the current pixel in each image $I_{1}$ and $I_{2}$
- $C_{1}$ is a constant computed with the choosen range $R$ and the parameter $k_{1}$ as follow : $C_{1} = \left ( k_{1}R \right )^{2}$
- $C_{2}$ is a constant computed with the choosen range $R$ and the parameter $k_{2}$ as follow : $C_{2} = \left ( k_{2}R \right )^{2}$
Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. "Image quality assessment: From error visibility to structural similarity". IEEE Transactions on Image Processing , vol. 13, pp. 600-612, Apr. 2004.
See also
Function Syntax
This function returns outputMeasurement.
// Function prototype
SsimValueMsr::Ptr ssimValue3d( std::shared_ptr< iolink::ImageView > inputImage1, std::shared_ptr< iolink::ImageView > inputImage2, iolink::Vector3u32 tileSize, SsimValue3d::RangeMode rangeMode, double range, double k1, double k2, SsimValueMsr::Ptr outputMeasurement = nullptr );
This function returns outputMeasurement.
// Function prototype.
ssim_value_3d(input_image1: idt.ImageType,
input_image2: idt.ImageType,
tile_size: Iterable[int] = [8, 8, 8],
range_mode: SsimValue3d.RangeMode = SsimValue3d.RangeMode.DATA_TYPE_RANGE,
range: float = 255,
k1: float = 0.01,
k2: float = 0.03,
output_measurement: Union[Any, None] = None) -> SsimValueMsr
This function returns outputMeasurement.
// Function prototype.
public static SsimValueMsr
SsimValue3d( IOLink.ImageView inputImage1,
IOLink.ImageView inputImage2,
uint[] tileSize = null,
SsimValue3d.RangeMode rangeMode = ImageDev.SsimValue3d.RangeMode.DATA_TYPE_RANGE,
double range = 255,
double k1 = 0.01,
double k2 = 0.03,
SsimValueMsr outputMeasurement = null );
Class Syntax
Parameters
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
 |
inputImage1 |
The first input image to compare. | Image | Grayscale | nullptr | ||||
|
inputImage2 |
The second input image to compare. | Image | Binary, Label, Grayscale or Multispectral | nullptr | ||||
|
tileSize |
The X, Y and Z sizes, in pixels, of the tile used to compute the SSIM for each pixel. | Vector3u32 | >=1 | {8, 8, 8} | ||||
|
rangeMode |
The way to consider the dynamic range used to compute the regularization constants C1 and C2 of the similarity formula.
|
Enumeration | DATA_TYPE_RANGE | |||||
|
range |
The data range used to compute the constants C1 and C2 of the similarity formula. It corresponds to the distance between the maximum and minimum of the representative image intensities. It is used only if rangeMode parameter is set to OTHER value. | Float64 | >0 | 255 | ||||
|
k1 |
The small weight used to compute the regularization constant C1 of the similarity formula. | Float64 | >0 | 0.01 | ||||
|
k2 |
The small weight used to compute the regularization constant C2 of the similarity formula. | Float64 | >0 | 0.03 | ||||
 |
outputMeasurement |
The output measurement results containing the SSIM value. | SsimValueMsr | nullptr | |||||
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
input_image1 |
The first input image to compare. | image | Grayscale | None | ||||
|
input_image2 |
The second input image to compare. | image | Binary, Label, Grayscale or Multispectral | None | ||||
|
tile_size |
The X, Y and Z sizes, in pixels, of the tile used to compute the SSIM for each pixel. | vector3u32 | >=1 | [8, 8, 8] | ||||
|
range_mode |
The way to consider the dynamic range used to compute the regularization constants C1 and C2 of the similarity formula.
|
enumeration | DATA_TYPE_RANGE | |||||
|
range |
The data range used to compute the constants C1 and C2 of the similarity formula. It corresponds to the distance between the maximum and minimum of the representative image intensities. It is used only if rangeMode parameter is set to OTHER value. | float64 | >0 | 255 | ||||
|
k1 |
The small weight used to compute the regularization constant C1 of the similarity formula. | float64 | >0 | 0.01 | ||||
|
k2 |
The small weight used to compute the regularization constant C2 of the similarity formula. | float64 | >0 | 0.03 | ||||
|
output_measurement |
The output measurement results containing the SSIM value. | SsimValueMsr | None | |||||
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
inputImage1 |
The first input image to compare. | Image | Grayscale | null | ||||
|
inputImage2 |
The second input image to compare. | Image | Binary, Label, Grayscale or Multispectral | null | ||||
|
tileSize |
The X, Y and Z sizes, in pixels, of the tile used to compute the SSIM for each pixel. | Vector3u32 | >=1 | {8, 8, 8} | ||||
|
rangeMode |
The way to consider the dynamic range used to compute the regularization constants C1 and C2 of the similarity formula.
|
Enumeration | DATA_TYPE_RANGE | |||||
|
range |
The data range used to compute the constants C1 and C2 of the similarity formula. It corresponds to the distance between the maximum and minimum of the representative image intensities. It is used only if rangeMode parameter is set to OTHER value. | Float64 | >0 | 255 | ||||
|
k1 |
The small weight used to compute the regularization constant C1 of the similarity formula. | Float64 | >0 | 0.01 | ||||
|
k2 |
The small weight used to compute the regularization constant C2 of the similarity formula. | Float64 | >0 | 0.03 | ||||
|
outputMeasurement |
The output measurement results containing the SSIM value. | SsimValueMsr | null | |||||
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" );
SsimValue3d ssimValue3dAlgo;
ssimValue3dAlgo.setInputImage1( foam );
ssimValue3dAlgo.setInputImage2( foam_gaussian_noise );
ssimValue3dAlgo.setTileSize( {6, 6, 6} );
ssimValue3dAlgo.setRangeMode( SsimValue3d::RangeMode::DATA_TYPE_RANGE );
ssimValue3dAlgo.setRange( 255 );
ssimValue3dAlgo.setK1( 0.01 );
ssimValue3dAlgo.setK2( 0.03 );
ssimValue3dAlgo.execute();
std::cout << "ssim: " << ssimValue3dAlgo.outputMeasurement()->ssim( 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"))
ssim_value_3d_algo = imagedev.SsimValue3d()
ssim_value_3d_algo.input_image1 = foam
ssim_value_3d_algo.input_image2 = foam_gaussian_noise
ssim_value_3d_algo.tile_size = [6, 6, 6]
ssim_value_3d_algo.range_mode = imagedev.SsimValue3d.DATA_TYPE_RANGE
ssim_value_3d_algo.range = 255
ssim_value_3d_algo.k1 = 0.01
ssim_value_3d_algo.k2 = 0.03
ssim_value_3d_algo.execute()
print("ssim: ", str(ssim_value_3d_algo.output_measurement.ssim(0)))
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );
ImageView foam_gaussian_noise = Data.ReadVipImage( @"Data/images/foam_gaussian_noise.vip" );
SsimValue3d ssimValue3dAlgo = new SsimValue3d
{
inputImage1 = foam,
inputImage2 = foam_gaussian_noise,
tileSize = new uint[]{6, 6, 6},
rangeMode = SsimValue3d.RangeMode.DATA_TYPE_RANGE,
range = 255,
k1 = 0.01,
k2 = 0.03
};
ssimValue3dAlgo.Execute();
Console.WriteLine( "ssim: " + ssimValue3dAlgo.outputMeasurement.ssim( 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 = ssimValue3d( foam, foam_gaussian_noise, {6, 6, 6}, SsimValue3d::RangeMode::DATA_TYPE_RANGE, 255, 0.01, 0.03 );
std::cout << "ssim: " << result->ssim( 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.ssim_value_3d(foam, foam_gaussian_noise, [6, 6, 6], imagedev.SsimValue3d.DATA_TYPE_RANGE, 255, 0.01, 0.03)
print("ssim: ", str(result.ssim(0)))
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );
ImageView foam_gaussian_noise = Data.ReadVipImage( @"Data/images/foam_gaussian_noise.vip" );
SsimValueMsr result = Processing.SsimValue3d( foam, foam_gaussian_noise, new uint[]{6, 6, 6}, SsimValue3d.RangeMode.DATA_TYPE_RANGE, 255, 0.01, 0.03 );
Console.WriteLine( "ssim: " + result.ssim( 0 ) );