Quantitative image quality evaluation of MR images using perceptual difference models

Image Watermarking using Chaotic Watermark Scrambling and Perceptual Quality Evaluation

In this report, a watermarking method for grayscale images is proposed that is invisible and robust to certain attacks. Chaotic maps are used to generate the watermark which improves the security of the method. The crown watermark is embedded in the real part of DFT domain and the embedding position is determined by the SURF algorithm. The peak signal-to-noise ratio is used to evaluate the perc...

Perceptual Criteria for Image Quality Evaluation

Evaluation of compressed lung CT image quality using quantitative analysis.

The goals of this study were (1) to evaluate the quality of compressed lung CT images obtained using high resolution CT (HRCT: 2 mm slice thickness) for degree of compression and conventional CT (10 mm slice thickness) images by using physical and subjective evaluations, and (2) to analyze the distortion of density distribution on lung CT images using histogram analysis for each compression rat...

Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for percep...

A Quantitative Evaluation of Image Segmentation Quality

Segmentation is often a procedure to group spatially adjacent image pixels into segments. Spatially adjacent pixels form one image segment if they meet some criteria, such as spectral similarity. A segmentation result may vary, depending on a given generalization level and other constraints such as compactness. In practice, the quality of a segmentation result is often assessed visually by the ...