Image Semantic Quality Assessment for Car-plate Recognition

• Main Authors: Yang, Nien-Tzu, 楊念慈
• Other Authors: Tsai, Wen-Jiin
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/82692897393966500526

碩士 === 國立交通大學 === 多媒體工程研究所 === 104 === In this thesis, we proposed an image semantic quality assessment method for car-plate images. The purpose of our method is to evaluate whether the characters in a car-plate image can be recognized or not after compressed. To this end, we considered that the compressed car-plate image has to be calculated from semantic-related features, rather than pixel-wised (ex. PSNR) features or structure-wised features (ex. SSIM). The proposed image semantic quality assessment (ISQA) method is based on car-plate recognition (CPR) techniques. By considering text locations, our algorithm combines high density detail blocks with blur to calculate the quality score for compressed car-plate images. The result can be applied to judge whether lower bitrates can be used in image compression to achieve the same recognition results, and hence improve the image coding efficiency. The proposed image semantic quality assessment method (ISQA) has been compared to some related image quality assessment metrics, and the result shows that both Spearman and Kendall correlation coefficients can be improved significantly.