An Estimation Method of Measuring Image Quality for Compressed Images of Human Face
This work addresses image quality assessment for compressed face images, which is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of measuring image quality for compressed human face images by comparing face and other regions to the original, finding that the face region is less compressed than other regions using quality indices like SSIM and G-SSIM.
Nowadays digital image compression and decompression techniques are very much important. So our aim is to calculate the quality of face and other regions of the compressed image with respect to the original image. Image segmentation is typically used to locate objects and boundaries (lines, curves etc.)in images. After segmentation the image is changed into something which is more meaningful to analyze. Using Universal Image Quality Index(Q),Structural Similarity Index(SSIM) and Gradient-based Structural Similarity Index(G-SSIM) it can be shown that face region is less compressed than any other region of the image.