近年來，由於數位相機與手機內建的相機的盛行，拍照變得越來越簡單，而照片的數量也大量的增加。照片可以用來表達拍照者的情感與對美學的敏感度，其中照片美學成了一個很重要的議題。照片美學的目的為將好或壞的照片做分類，幫助一般拍照使用者也可以拍出高評價、專業的照片吸引大家的目光。

在本篇論文提出了一個照片複雜程度評估的方法，使用如Sobel的邊緣偵測及Harris角點偵測等等方法來評估複雜程度，這邊總共有155個特徵點被截取出來。再來針對相關文獻做的色彩豐富度的評估做了改良，不僅改良了原本的算法，也另外再加入一些重要的特徵，這邊總共有94個特徵點被截取出來，最後結合模糊度評估共635維，分類器的部分使用SVM方法辨識出這張照片是高品質或是低品質的照片。

實驗的部分，本文使用兩個公開影像資料庫，分別是CUHKPQ及CUHK。影像資料庫CUHKPQ按照7種不同主題的照片做分類，CUHK這資料庫則不對主題做分類，最後實驗證明本文提出的方法可以有效地對高低品質照片做分類，CUHK的正確率為88.7%，CUHKPQ的正確率也在90%~96.4%之間。

In recent years, due to the popularity of digital still camera and camera module of the mobile phone, taking photos is easier than ever and the number of photos are growing exponentially. Photography is a form of art that the photographers can convey their emotion and aesthetic sensibilities. Photo aesthetics has become a very important issue. The purpose of photo aesthetics is to classify good or bad photos. And lets general camera users shoot a high quality and professional photo to catch people’s eyes.

In our paper, we present a method of image complexity assessment. We use Sobel edge detection method and Harris corner points method to extract the image feature detection and so on to assess the image complexity. In this part, we extracted 155 feature dimensions. And then, we improve the “Color & Composition” features in previous works. We not only improve the original feature extraction method but also added some feature extraction method to extract the feature. In this part, we extracted 94 feature dimensions. We extracted 635 feature dimensions in blur assessment. Finally, we use SVM to classify the images as high or low quality images.

In our experiments, we use image databases CUHKPQ and CUHK respectively. The CUHKPQ dataset is divided into seven thematic categories. CUHK dataset collected images from all categories. The proposed method can correctly classify the high or low quality images. CHUK accuracy is 88.72% and CUHKPQ accuracy is between 90% and 96.4%.

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