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Research Article

Distorted Image Classification Using No-Reference Image Quality Assessment Based on Spatial Domain Transformation of Feature Informations

특징 정보 공간 도메인 변환 기반의 무 기준 영상 품질 평가를 이용한 왜곡 영상 분류

Do-Kyung Shin1, Jae-Kyung Kim2

신도경1, 김재경2

1Ph.D./Research Engineer, Aviation Drone Lab, LIG Nex1
2Principal Research Engineer, Aviation Drone Lab, LIG Nex1
1LIG 넥스원 항공드론연구소 선임연구원/공학박사
2LIG 넥스원 항공드론연구소 수석연구원
30 June 2021. pp. 49-69
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Abstract
본 논문에서는 영상 품질에 대한 신뢰성을 확보하기 위해 색상 및 블러 특징정보 공간변환 기반의 무 기준 영상 품질 평가 방법을 제안한다. 영상데이터의 활용분야가 증가함에 따라 데이터의 양도 급증하였으며 영상 데이터의 품질은 데이터의 활용 결과에도 큰 영향을 미치기 때문에 사전에 고품질의 영상을 분류할 수 있는 단계가 반드시 필요하다. 기존에 제안된 방법들은 단순히 품질평가 점수가 높거나 낮을 때 영상의 품질이 좋음을 의미하지만 품질이 좋은 영상과 왜곡된 영상을 분류하기에는 어려움이 존재한다. 따라서 본 논문에서는 영상 품질 평가를 위해 가장 많이 사용되는 4개의 데이터 셋과 DMOS 정보를 이용하여 품질의 신뢰성이 낮은 왜곡 영상을 분류하는 방안을 제안함으로써 기존의 문제점을 해결하고자 한다. 분류 성능의 정확도를 분석하기 위해 제안 알고리즘과 기존 영상 품질 평가(IQA) 알고리즘을 동일한 평가 방법으로 비교하였으며, 제안하는 알고리즘이 기존 영상 품질 평가 알고리즘보다 높은 우수성을 보임을 확인하였다.
In this paper, we propose a no-reference image quality assessment algorithm based on spatial transformation of color and blur feature information in order to secure the reliability of the quality. As a field of application of image data increased, the amount of data also increased rapidly. In addition, since the quality of image data has a great influence on the result, it is necessary to classify high-quality images in advance. Existing methods simply mean that the quality of the image is good when the score of image quality assessment is high or low. However, there is a difficulty in classifying images as having good or bad quality. Therefore, we try to solve the problems of the existing algorithms by proposing an algorithm to classify distortion images with low quality reliability, and use four data sets of CSIQ, KADID, LIVE, TID2013 and DMOS, which are known most. In order to analyze the accuracy of classification performance, this method was applied equally to the existing methods and compared, and the proposed method was shown to be higher than that of the existing methods.
Keywords
Image Quality Assessment
No-reference
Distortion image
Image classification
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Information
  • Publisher :The Society of Convergence Knowledge
  • Publisher(Ko) :융복합지식학회
  • Journal Title :The Society of Convergence Knowledge Transactions
  • Journal Title(Ko) :융복합지식학회논문지
  • Volume : 9
  • No :2
  • Pages :49-69
  • DOI :https://doi.org/10.22716/sckt.2021.9.2.014
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