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Screen content image quality measurement based on multiple features

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Abstract

With the increasing use of multimedia devices, the demand for Screen Content Images (SCIs) has surged. However, the transmission process inevitably leads to visual degradation of image quality. Effective measurement of the quality of SCIs is therefore an urgent task. In this paper, we propose an image quality measurement method based on multiple features. According to the content characteristics of SCIs, we extract multiple features in terms of both structure and color. As SCIs contain a large amount of text and graphics, we calculate different gradient-weighted local ternary pattern histograms on the gradient domain to capture the structural degradation of the image from various aspects. Then, considering color as another crucial visual factor, we extract contrast energy and saturation from the opponent color space, and design parameter models that can accurately characterize the color information of SCIs. Finally, we use the Adaboost-BP neural network to train the quality measurement model. Experimental comparisons on three public SCIs databases (SIQAD, SCID, QACS) demonstrate that the proposed method is more in line with human perception compared to other state-of-the-art quality metrics. In addition, we demonstrate in the experiment that the proposed method can be a better alternative to the Peak Signal-to-Noise Ratio (PSNR) to assess the visual quality of watermarked SCIs in practice applications.

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Data Availability

All data generated or analyzed during this study are included in this article. The code and databases are located in the following link: https://drive.google.com/drive/folders/1AsOmS_FOIefGbqlfknYjQa_dsHkbc04a?usp=sharing

Abbreviations

ABNN:

Adaboost-BP Neural Network

BIQM:

Blind Image Quality Measurement

BP:

Back Propagation

CC:

Contrast Change

CNN:

Convolutional Neural Network

CQD:

Color Quantization with Dithering

CSC:

Color Saturation Change

DMOS:

Differential Mean Opinion Score

FR-IQM:

Full Reference Image Quality Measurement

GB:

Gaussian Blur

GGD:

Generalized Gaussian Distribution

GN:

Gaussian Noise

HEVC:

High-Efficiency Video Coding

HVS:

Human Visual System

IQM:

Image Quality Measurement

JPEG:

Joint Photographic Experts Group

JP2K:

JPEG2000

LBP:

Local Binary Pattern

LSC:

Layer-Segmentation based Compression

LTP:

Local Ternary Pattern

MB:

Motion Blur

MOS:

Mean Opinion Score

MSCN:

Mean Subtracted Contrast Normalized

NSS:

Natural Scene Statistics

OC:

Opponent Color

OU:

Opinion-Unaware

PDF:

Probability Density Function

PLCC:

Pearson Linear Correlation Coefficient

PSNR:

Peak Signal-to-Noise Ratio

QACS:

Quality Assessment of Compressed SCI

QoE:

Quality of Experience

RMSE:

Root Mean Squared Error

SCC:

Screen Content Compression

SCID:

Screen Content Image Database

SC-IQM:

Screen Content Image Quality Measurement

SCIs:

Screen Content Images

SIQAD:

Screen Image Quality Assessment Database

SRCC:

Spearman Rank-order Correlation Coefficient

VLSD:

Variance of Local Standard Deviation

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 62272003 and in part by the Natural Science Foundation of the Anhui Higher Education Institutions of China under Grant KJ2021A0016.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui University, Hefei, 230601, China

    Yang Yang, Zhuoran Xu & Yunhao Zhang

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  1. Yang Yang
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  2. Zhuoran Xu
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  3. Yunhao Zhang
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Correspondence to Yang Yang.

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Appendix A

Appendix A

Fig. 15
figure 15

The scatter plots of different BIQM models for SCIs on SCID database

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Fig. 16
figure 16

The scatter plots of different BIQM models for SCIs on QACS database

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Yang, Y., Xu, Z. & Zhang, Y. Screen content image quality measurement based on multiple features. Multimed Tools Appl 83, 72623–72650 (2024). https://doi.org/10.1007/s11042-024-18366-x

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