Image quality assessment based on harmonics gain/loss information

Abstract

We present an objective reduced-reference image quality assessment method based on harmonic gain/loss information through a discriminative analysis of local harmonic strength (LHS). The LHS is computed from the gradient of images, and its value represents a relative degree of the appearance of blockiness on images when it is related to energy gain within an image. Furthermore, comparison between local harmonic strength values from an original, distortion-free image and a degraded, processed, or compressed version of the image shows that the LHS can also be used to indicate other types of degradations, such as blurriness that corresponds with energy loss. Our simulations show that we can develop a single metric based on this gain/loss information and use it to rate the quality of images encoded by various encoders such as DCT-based JPEG, wavelet-based JPEG 2000, or various processed images. We show that our method can overcome some limitations of the traditional PSNR.

Video Quality Metrics

“…Nevertheless, synchronization between the original and impaired data is still necessary. Works in this area include the work by Webster et al (Webster et al, 1993), Brètillon et al (Bretillon et al, 2000), Gunawan and Ghanbari (Gunawan & Ghanbari, 2005), and the work by Carnec et al (Carnec et al, 2003). In this section, we describe the RR metrics by Webster et al and Gunawan and Ghanbari.…”

“…The work by Gunawan and Ghanbari (Gunawan & Ghanbari, 2005) proposes a RR video quality metric that is based on a local harmonic strength (LHS) feature. The harmonic strength can be interpreted as a spatial activity measure, estimated in terms of vertical/horizontal edges of the picture.…”

Reduced reference image quality assessment metric using optimized parameterized wavelet watermarking

“…Since the original image is not available at the receiver end, fullreference quality metrics cannot be used. Reducedreference (RR) quality metrics [2][3][4][5][6][7][8][9][10], however, can be used by sending specific features of the original image (embedded as a watermark) along with it to the receiver.…”

Vector Quantisation Based Image Enhancement

“…Since image expansion algorithms usually introduce blur artifacts, another quality metric (Gunawan and Ghanbari, 2005) which is able to detect and measure the degree of blurriness on image was also used. This metric uses features extracted from the frequency domain through two-dimensional Discrete Fourier Transfrom (DFT) computation over a localised area on the gradient image.…”