计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
16-20,25
,共6页
特征提取%相位一致性%边缘检测%图像质量评价
特徵提取%相位一緻性%邊緣檢測%圖像質量評價
특정제취%상위일치성%변연검측%도상질량평개
feature extraction%phase congruency%edge detection%image quality assessment
各种图像处理建立在有效性地提取图像特征之上，如图像分类、分割和图像质量评价等，因此获取有效的图像特征对于图像处理意义十分重大。不同于在图像灰度的突变点处直接定义图像特征，相位一致性（PC）在傅里叶分量的相位保持高度一致的位置观测图像特征，获得了丰富的特征信息和精确的特征定位，与人类视觉系统（HVS）对图像特征的认知相符。提出了一种新的基于相位一致性特征的图像质量评价方法。该方法使用退化与参考图像的相位一致性在局部区域的相似度来测量图像质量的退化程度；并且考虑到相位一致性是纹理和边缘的反应，而人类视觉系统对纹理丰富的区域较为敏感，利用相位一致的局部最值作为加权值，将局部的相似度结合为单个的图像质量评分值。实验结果表明，提出的图像质量指标具有较好的主客观一致性。尤为重要的是，该指标对图像的亮度和对比度变化不敏感。
各種圖像處理建立在有效性地提取圖像特徵之上，如圖像分類、分割和圖像質量評價等，因此穫取有效的圖像特徵對于圖像處理意義十分重大。不同于在圖像灰度的突變點處直接定義圖像特徵，相位一緻性（PC）在傅裏葉分量的相位保持高度一緻的位置觀測圖像特徵，穫得瞭豐富的特徵信息和精確的特徵定位，與人類視覺繫統（HVS）對圖像特徵的認知相符。提齣瞭一種新的基于相位一緻性特徵的圖像質量評價方法。該方法使用退化與參攷圖像的相位一緻性在跼部區域的相似度來測量圖像質量的退化程度；併且攷慮到相位一緻性是紋理和邊緣的反應，而人類視覺繫統對紋理豐富的區域較為敏感，利用相位一緻的跼部最值作為加權值，將跼部的相似度結閤為單箇的圖像質量評分值。實驗結果錶明，提齣的圖像質量指標具有較好的主客觀一緻性。尤為重要的是，該指標對圖像的亮度和對比度變化不敏感。
각충도상처리건립재유효성지제취도상특정지상，여도상분류、분할화도상질량평개등，인차획취유효적도상특정대우도상처리의의십분중대。불동우재도상회도적돌변점처직접정의도상특정，상위일치성（PC）재부리협분량적상위보지고도일치적위치관측도상특정，획득료봉부적특정신식화정학적특정정위，여인류시각계통（HVS）대도상특정적인지상부。제출료일충신적기우상위일치성특정적도상질량평개방법。해방법사용퇴화여삼고도상적상위일치성재국부구역적상사도래측량도상질량적퇴화정도；병차고필도상위일치성시문리화변연적반응，이인류시각계통대문리봉부적구역교위민감，이용상위일치적국부최치작위가권치，장국부적상사도결합위단개적도상질량평분치。실험결과표명，제출적도상질량지표구유교호적주객관일치성。우위중요적시，해지표대도상적량도화대비도변화불민감。
In practical image processing, some post-processing operations largely depend on the availability of image fea-tures. Operations, like classification, segmentation, and image quality assessment, are often carried out in a feature space. The availability of image features plays an important role for further analysis. Rather than defining features directly at points with sharp change in intensity, the Phase Congruency(PC), which is a dimensionless measure of the significant of a local structure, postulates that features are perceived at points where the Fourier components are maximal in phase. The PC model is in accordance with the human visual system that demonstrates good invariance to light conditions and can achieve effective feature information and well feature location accuracy. Based on those properties, it proposes a new image quality metric based on the PC model, which utilizes the local similarities of phase congruency between the reference and distorted image to quantify the image distortion. Moreover, considering that human visual system is sensitive to phase con-gruent structures and that the PC value at a location can reflect how likely it is a perceptibly significant structure point, PC values at a location are employed to combine the similarities within local regions into a single quality score. The experi-mental result shows that the proposed algorithm is correlated well with the judgment of human observers. More importantly, the metric is invariant to changes in image brightness or contrast.
