This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and bitmap images, which are further, processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database.
The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Best Android and PHP Projects for the final year college students Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content based image retrieval system.
Many former schemes have been developed to improve the retrieval accuracy in the content-based image retrieval (CBIR) system. One type of them is to employ image features derived from the compressed data stream as opposite to the classical approach that extracts an image descriptor from the original image; this retrieval scheme directly generates image features from the compressed stream without first performing the decoding process. This type of retrieval aims to reduce the time computation for feature extraction/generation since most of the multimedia images are already converted to compressed domain before they are recorded in any storage devices. In the image features are directly constructed from the typical block truncation coding (BTC) or halftoning-based BTC compressed data stream without performing the decoding procedure.
These image retrieval schemes involve two phases, indexing and searching, to retrieve a set of similar images from the database.
The indexing phase extracts the image features from all of the images in the database which is later stored in database as feature vector. In the searching phase, the retrieval system derives the image features from an image submitted by a user (as query image), which are later utilized for performing similarity matching on the feature vectors stored in the database. The image retrieval system finally returns a set of images to the user with a specific similarity criterion, such as color similarity and texture similarity. The concept of the BTC is to look for a simple set of representative vectors to replace the original images. Specifically, the BTC compresses an image into a new domain by dividing the original image into multiple nonoverlapped image blocks, and each block is then represented with two extreme quantizers (i.e., high and low mean values) and bitmap image. Two subimages constructed by the two quantizers and the corresponding bitmap image are produced at the end of BTC encoding stage, which are later transmitted into the decoder module through the transmitter. Learn and develop projects with full support To generate the bitmap image, the BTC scheme performs thresholding operation using the mean value of each image block such that a pixel value greater than the mean value is regarded as 1 (white pixel) and vice versa.
The traditional BTC method does not improve the image quality or compression ratio compared with JPEG or JPEG 2000. However, the BTC schemes achieve much lower computational complexity compared with that of these techniques. Some attempts have been addressed to improve the BTC reconstructed image quality and compression ratio, and also to reduce the time computation. Even though the BTC scheme needs low computational complexity, it often suffers from blocking effect and false contour problems, making it less satisfactory for human perception. The halftoning-based BTC, namely, error diffusion BTC (EDBTC) is proposed to overcome the two above disadvantages of the BTC. Similar to the BTC scheme, EDBTC looks for a new representation (i.e., two quantizers and bitmap image) for reducing the storage requirement. The EDBTC bitmap image is constructed by considering the quantized error which diffuses to the nearby pixels to compensate the overall brightness, and thus, this error difussion strategy effectively removes the annoying blocking effect and false contour, while maintaining the low computational complexity.
The low-pass nature of human visual system is employed in to access the reconstructed image quality, in which the continuous image and its halftone version are perceived similarly by human vision when these two images viewed from a distance. The EDBTC method divides a given image into multiple nonoverlapped image blocks and each block is processed independently to obtain two extreme quantizers. This unique feature of independent processing enables the parallelism scenario. In bitmap image generation step, the pixel values in each block are thresholded by a fixed average value in the block with employing error kernel to diffuse the quantization error to the neighboring pixels during the encoding stage. A new image retrieval system has been proposed for the color image.
Three feature descriptors, namely, structure element correlation (SEC), gradient value correlation (GVC), and gradient direction correlation (GDC) are utilized to measure the similarity between the query and the target images in database. This indexing scheme provides a promising result in big database and outperforms the former existing approaches, as reported in the method in compresses a grayscale image by combining the effectiveness of fractal encoding, discrete cosine transform (DCT), and standard deviation of an image block. An auxiliary encoding algorithm has also been proposed to improve the image quality and to reduce the blocking effect. As reported in this new encoding system achieves a good coding gain as well as the promising image quality with very efficient computation. In a new method for tamper detection and recovery is proposed utilizing the DCT coefficient, fractal coding scheme, and the matched block technique. More info here This new scheme yields a higher tampering detection rate and achieves good restored image quality, as demonstrated in combines the fractal image compression and wavelet transform to reduce the time computation in image encoding stage.
This method produces a good image quality with a fast encoding speed, as reported in the fast and efficient image coding with the no-search fractal coding strategies have been proposed methods employ the modified graylevel transform to improve the successful matching probability between the range and domain block in the fractal coding. Two gray-level transforms on quadtree partition are used in to achieve a fast image coding and to improve the decoded image quality. The method in exploits a fitting plane method and a modified gray-level transform to speedup the encoding process. The fractal image coding presented in accelerates the image encoding stage, reduces the compression ratio, and simultaneously improves the reconstructed image quality. A fast fractal coding is also proposed in which utilizes the matching error threshold. This method first reduces the codebook capacity and takes advantage of matching error threshold to shorten the encoding runtime. The method in can achieve a similar or better decoded image with the fast compression process compared with the conventional fractal encoding system with full search strategy.
The contributions can be summarized as follows: 1) extending the EDBTC image compression technique for the color image; 2) proposing two feature descriptors, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), which can be directly derived from the EDBTC compressed data stream without performing decoding process; and 3) presenting a new low complexity joint CBIR system and color image compression by exploiting the superiority of EDBTC scheme. The rest of this paper is organized as follows. Click to read A brief introduction of EDBTC is provided in Section II. Section III presents the proposed EDBTC image retrieval including the image feature generation and accuracy computation. Extensive experimental results are reported at Section IV. Finally, the conclusion is drawn at the end of this paper.