Skip to main content

A Perceptually Relevant No-Reference Blockiness Metric Based on Local Image Characteristics

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.

Publisher note

To access the full article, please see PDF.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hantao Liu.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Liu, H., Heynderickx, I. A Perceptually Relevant No-Reference Blockiness Metric Based on Local Image Characteristics. EURASIP J. Adv. Signal Process. 2009, 263540 (2009). https://doi.org/10.1155/2009/263540

Download citation

Keywords

  • Quantum Information
  • Computational Efficiency
  • Subjective Data
  • Human Visual System
  • Computation Efficiency