Grading Image Retrieval Based on DCT and DWT Compressed Domains Using Low-Level Features

Abstract

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the highperformance of discrete wavelet transform (DWT). It is timeconsuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms-Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications

“…The texture feature based on wavelet transform provides a better performance and stability, according to the testing results. Wang et al [ 22 ] proposed image retrieval based on DCT and DWT with feature extraction utilising grading algorithms in 2015. The color moments, color histogram, and a novel dynamic color space quantization based on color distribution were modified to generate a color feature in the DCT domain, while the texture feature was computed using the DWT domain.…”

A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform

“…Also it is known that DWT is very fast computing transformation tool [9], hence most RBIR systems are based on wavelet and related wavelet coefficients. However, image retrieval approaches based on DWT, are not sensitive to edge singularities [10,11,12]. For this reason two alternatives are provided, one is using Gabor filters [13], and the other is using Curvelet transform [9].…”