Abstract
A novel no-reference blockiness metric that provides a quantitative measure of blocking annoyance in block-based DCT coding is presented. The metric incorporates properties of the human visual system (HVS) to improve its reliability, while the additional cost introduced by the HVS is minimized to ensure its use for real-time processing. This is mainly achieved by calculating the local pixel-based distortion of the artifact itself, combined with its local visibility by means of a simplified model of visual masking. The overall computation efficiency and metric accuracy is further improved by including a grid detector to identify the exact location of blocking artifacts in a given image. The metric calculated only at the detected blocking artifacts is averaged over all blocking artifacts in the image to yield an overall blockiness score. The performance of this metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data at a reduced computational load. As such, the proposed blockiness metric is promising in terms of both computational efficiency and practical reliability for real-life applications.
Highlights
Objective metrics, which serve as computational alternatives for expensive image quality assessment by human subjects, aimed at predicting perceived image quality aspects automatically and quantitatively
A psychovisual experiment was conducted to assign to each image a mean opinion quality score (MOS) measured on a continuous linear scale that was divided into five intervals marked with the adjectives “Bad,” “Poor,”, “Fair,” “Good,” and “Excellent.”
This image patch had a grid of blocks of 8 × 8 pixels starting at its top-left corner, and it clearly exhibited visible blocking artifacts. It was scaled up with a factor 4/3 × 7/3, resulting in an image with an effective block size of 11 × 19 pixels. Blocking annoyance in this scaled image was estimated with three metrics, that is, no-reference perceptual blockiness metric (NPBM), generalized block-edge impairment metric (GBIM), and LABM
Summary
Objective metrics, which serve as computational alternatives for expensive image quality assessment by human subjects, aimed at predicting perceived image quality aspects automatically and quantitatively. A second metric using the spatial domain is based on a locally adaptive algorithm [16] and is hereafter referred to as LABM It calculates a blockiness metric for each individual coding block in an image and simultaneously estimates whether the blockiness is strong enough to be visible to the human eye by means of a just-noticeable-distortion (JND) profile. A novel algorithm is proposed to quantify blocking annoyance based on its local image characteristics It combines existing ideas in literature with some new contributions: (1) a refined pixel-based distortion measure for each individual blocking artifact in relation to its direct vicinity; (2) a simplified and more efficient visual masking model to address the local visibility of blocking artifacts to the human eye; (3) the calculation of the local pixelbased distortion and its visibility on the most relevant stimuli only, which significantly reduces the computational cost.
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