sharpedge.pca_compression

Functions

pca_compression(img[, preservation_rate])

Compress the input image using Principal Component Analysis (PCA) via the Singular Value Decomposition (SVD) method.

Module Contents

sharpedge.pca_compression.pca_compression(img, preservation_rate=0.9)[source]

Compress the input image using Principal Component Analysis (PCA) via the Singular Value Decomposition (SVD) method. This function first applies SVD to decompose the image array into its principal components, and then retains a specified portion of the eigenvectors based on the preservation rate. The output is the compressed image in 2D array. This function supports only grayscale (2D) images.

Parameters:

  • img (numpy.ndarray) – Input image array. This needs to be a 2D numpy array (grayscale image).

  • preservation_rate (float, optional) – The proportion of eigen values to preserve in the compressed image. Must be a value between 0 and 1. Higher values preserve more details from the original image, while lower values result in greater compression. Default is 0.9.

Returns:

A numpy array representing the manipulated image. The output would be: - A grayscale image (2D array).

Return type:

numpy.ndarray

Raises:

  • TypeError – If preservation_rate is not a float.

  • ValueError – If the input image is not valid (not a 2D array). If preservation_rate is not between 0 and 1.

  • Warning – If preservation_rate is very low (< 0.1), as it may result in significant quality loss.

Examples

Compress a grayscale image by retaining 80% of the variance: >>> compressed_img = pca_compression(img, preservation_rate=0.8)

Compress an RGB image with the default preservation rate (90%): >>> compressed_img = pca_compression(img)