How to Calculate Network PSNR in omnet++

To calculate the network Peak Signal-to-Noise Ratio (PSNR) in OMNeT++ has numerous steps that usually used to measure the quality of received images or video frames that have been transmitted over a network. The PSNR is the common parameters to evaluate the quality of reconstruction of lossy compression like in video or image transmission associated to the original signal. Get complete project help from omnet-manual.com we have all the necessary tool to complete your work. The given below are the procedures on how to calculate the PSNR in OMNeT++:

Steps to Calculate PSNR in OMNeT++

1. Understand PSNR Formula:
   • The PSNR is deliberate using the Mean Squared Error (MSE) among the original signal (image or video frame) and the received (or reconstructed) signal.

PSNR=10⋅log⁡10(MAX2MSE)\text{PSNR} = 10 \cdot \log_{10}\left(\frac{\text{MAX}^2}{\text{MSE}}\right)PSNR=10⋅log10​(MSEMAX2​)

Where:

1. • MAX is the maximum possible pixel value of the image such as 255 for an 8-bit image.
   • MSE is the Mean Squared Error among the original and received images.

The MSE is calculated as:

MSE=1MN∑i=0M−1∑j=0N−1[I(i,j)−K(i,j)]2\text{MSE} = \frac{1}{MN} \sum_{i=0}^{M-1} \sum_{j=0}^{N-1} \left[I(i,j) – K(i,j)\right]^2MSE=MN1​i=0∑M−1​j=0∑N−1​[I(i,j)−K(i,j)]2

Where:

1. • I(i,j)I(i,j)I(i,j) is the pixel value at position (i,j)(i,j)(i,j) in the original image.
   • K(i,j)K(i,j)K(i,j) is the pixel value at position (i,j)(i,j)(i,j) in the received image.
   • MMM and NNN are the dimensions of the image.
2. Implementing in OMNeT++:
   • Capture and Process Images: During OMNeT++ simulation, we need to capture both the original and the received images (or video frames) for comparison.

// Example pseudocode for handling an image

int width = 640;  // Example image width

int height = 480; // Example image height

int maxPixelValue = 255; // Max pixel value for 8-bit images

// Arrays to store original and received image data

std::vector<std::vector<int>> originalImage(height, std::vector<int>(width));

std::vector<std::vector<int>> receivedImage(height, std::vector<int>(width));

1. • Calculate MSE: For each pixel in the original and received images, estimate the squared difference and then average it to get the MSE.

double mse = 0.0;

for (int i = 0; i < height; i++) {

for (int j = 0; j < width; j++) {

mse += pow(originalImage[i][j] – receivedImage[i][j], 2);

}

}

mse = mse / (width * height);

1. • Calculate PSNR: Use the MSE to estimate the PSNR.

double psnr = 10 * log10((maxPixelValue * maxPixelValue) / mse);

1. Recording and Analysing PSNR:
   • Record the computed PSNR for each frame or image. We need to use OMNeT++’s recordScalar function to save the PSNR values for later analysis.

recordScalar(“Network PSNR (dB)”, psnr);

EV << “Network PSNR: ” << psnr << ” dB” << endl;

1. Example Implementation in OMNeT++:

class VideoReceiverApp : public cSimpleModule {

private:

int width, height;

int maxPixelValue;

std::vector<std::vector<int>> originalImage;

std::vector<std::vector<int>> receivedImage;

protected:

virtual void initialize() override {

width = 640;   // Example dimensions

height = 480;

maxPixelValue = 255;

originalImage.resize(height, std::vector<int>(width, 0));  // Initialize with some original image data

receivedImage.resize(height, std::vector<int>(width, 0));  // Initialize with received image data

}

virtual void handleMessage(cMessage *msg) override {

// Assume msg contains received image data; populate receivedImage with it.

// Calculate MSE

double mse = 0.0;

for (int i = 0; i < height; i++) {

for (int j = 0; j < width; j++) {

mse += pow(originalImage[i][j] – receivedImage[i][j], 2);

}

}

mse = mse / (width * height);

// Calculate PSNR

double psnr = 10 * log10((maxPixelValue * maxPixelValue) / mse);

// Record and display PSNR

recordScalar(“Network PSNR (dB)”, psnr);

EV << “Network PSNR: ” << psnr << ” dB” << endl;

delete msg;

}

};

1. Considerations:
   • Data Handling: Make sure that OMNeT++ model correctly manages the image or video data that might include to converting data formats or managing large amounts of data.
   • Real-Time PSNR Calculation: For video transmission, we need to calculate and log PSNR for each frame to measure the quality over time.

In this simulation, we know how to estimate the PSNR in the images and video frames over the network using the OMNeT++ tool. We will outline the steps involved in performing the PSNR in different simulation settings.