  Content-based image retrieval (CBIR) systems on pixel domain use low-level
features, such as colour, texture and shape, to retrieve images. In this
context, two types of image representations i.e. local and global image
features have been studied in the literature. Extracting these features from
pixel images and comparing them with images from the database is very
time-consuming. Therefore, in recent years, there has been some effort to
accomplish image analysis directly in the compressed domain with lesser
computations. Furthermore, most of the images in our daily transactions are
stored in the JPEG compressed format. Therefore, it would be ideal if we could
retrieve features directly from the partially decoded or compressed data and
use them for retrieval. Here, we propose a unified model for image retrieval
which takes DCT coefficients as input and efficiently extracts global and local
features directly in the JPEG compressed domain for accurate image retrieval.
The experimental findings indicate that our proposed model performed similarly
to the current DELG model which takes RGB features as an input with reference
to mean average precision while having a faster training and retrieval speed.
