<title>Texture characterization of compressed aerial images using DCT coefficients</title>

Abstract

We test the performance of a texture feature constructed from the variance of the first eight AC Discrete Cosine Transform (DCT) coefficients of JPEG compressed images. We break the image into sub-images, consisting of many 8*8 blocks, and them calculate the variance of each DCT coefficient across the sub-image. We evaluate the texture feature at two different image resolutions, and at three different quality factors. In our high resolution image a pixel covered a square of side 4 cm on the ground. Our low resolution image was generated by subsampling. Representative feature vectors were generated for give subjectively identified textures, by averaging a small training set. Each sub-image was then classified according to the representative feature vector closest in feature space. Compression ratio had little effect on the classification result in our study. However image resolution significantly altered the classification result. Classification correlated much more closely to a subjective classification for the low resolution image. Feature vectors also fell into much more clearly defined clusters at the lower resolution. Although more research is required across different photo-scales and sets of images, we conclude that texture features generated from compressed JPEG images have potential for content-based image retrieval based on texture.