Ordered Dither Block Truncation Coding Research Articles

Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.

Phase congruency and ODBTC based image retrieval

In this study, the authors investigate content based image retrieval (CBIR) using ordered-dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall.

Halftoning-based BTC image reconstruction using patch processing with border constraint

This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as “border” and “non-border” type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality.

Reducing JPEG False Contour Using Visual Illumination

This paper presents an efficient and simple technique on reducing the false contour problem which often occurs in the JPEG decoded image. The false contour appears on JPEG decoded image while applying small quality factor. This problem induces unpleasant visual appearance. The proposed scheme exploits the usability of Halftoning-Based Block Truncation Coding (HBTC) approach on generating visual illumination to reduce the aforementioned problem. Three HBTC techniques, namely Ordered Dither Block Truncation Coding (ODBTC), Error Diffusion Block Truncation Coding (EDBTC) and Dot Diffused Block Truncation Coding (DDBTC), modify the DC components of all Discrete Cosine Transform (DCT) processed image on JPEG encoding stage. Experimental results show the proposed method superiority on JPEG false contour reduction. To further improve the JPEG decoded image quality, the proposed method utilizes the Gaussian kernel on replacing the DDBTC diffused kernel on spreading the error term. It assumes that the adjacent image blocks have a strong correlation in which high diffused coefficient should be applied on nearest adjacent neighbor. This paper extends the HBTC usability on JPEG false contour suppression.

Multi resolution feature combined with ODBTC technique for robust CBIR system

Content based image retrieval (CBIR) is a system that retrieves a set of images that most resembles the query image. The technology is used in many applications. Currently used image content retrieval method is ordered-dither block truncation coding (ODBTC). This method is used to produce image content descriptors. In this system, it gives only an average accuracy of 70.5%. Our aim is to create a more robust and accurate system for CBIR. For this purpose in addition to colour cooccurrence feature (CCF) and bit pattern features (BPF), contourlet and wavelet features from the query image is extracted for image retrieval process. In our experiment the system is first tested with ODBTC and wavelet and then ODBTC and contourlet. The results obtained with ODBTC and contourlet is more accurate and produced accuracy 91.5%. The dataset used for our experiment is CorelDB.

A Review on Image Compression Using Halftoning Based BTC

In this paper scrutinizes image compression using Halftoning Based Block Truncation Coding for color image. Many algorithms were selected likely the original Block Truncation coding, Ordered Dither Block Truncation Coding, Error Diffusion Block Truncation Coding , and Dot Diffused Block Truncation Coding . These above techniques are divided image into non overlapping blocks. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence halftoning based block truncation coding (HBTC) is used to overcome the two issues. Objective measures are used to evaluate the image degree of excellence such as Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index and Compression Ratio. At the end, conclusions have shown that the Dot Diffused Block Truncation Coding algorithm outperforms the Block Truncation Coding as well as Error Diffusion Block Truncation Coding.

A Comparative Study on Image Compression Using Half toning Based Block Truncation Coding for Color Image

In this paper scrutinizes image compression using Halftoning Based Block Truncation Coding for color image. Many algorithms were selected likely the original Block Truncation coding, Ordered Dither Block Truncation Coding, Error Diffusion Block Truncation Coding , and Dot Diffused Block Truncation Coding. These above techniques are divided image into non overlapping blocks. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence halftoning based block truncation coding (HBTC) is used to overcome the two issues. Objective measures are used to evaluate the image degree of excellence such as Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index and Compression Ratio. At the end, conclusions have shown that the Dot Diffused Block Truncation Coding algorithm outperforms the Block Truncation Coding as well as Error Diffusion Block Truncation Coding.

Image retrieval using indexed histogram of Void-and-Cluster Block Truncation Coding

This paper presents a simple approach to improve the image retrieval accuracy in the Void-and-Cluster Block Truncation Coding compressed domain. The proposed approach directly derives an image descriptor from the Ordered Dither Block Truncation Coding (ODBTC) data stream without performing the decoding process. The Color Histogram Feature (CHF) is generated from the two ODBTC color quantizer, while the Halftoning Local Derivative Pattern (HLDP) is constructed from the ODBTC bitmap image. The similarity between two images are measured from their CHF and HLDP features. Three schemes are involved to improve the image retrieval accuracy, including the similarity weight optimization, feature reweighting, and user relevance feedback optimization. An evolutionary stochastic algorithm is exploited to optimize the similarity weight and feature weight in the nearest neighbor distance computation, as well as in the query update of relevance feedback optimization. Section 5 shows that the proposed scheme yields a promising result, and thus it can be a very effective candidate in addressing the content-based image retrieval and image classification task.

Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.

Image indexing using the color and bit pattern feature fusion

This paper presents a new way to index a color image by exploiting the low complexity of the Ordered-Dither Block Truncation Coding (ODBTC) for generating the image features. Image content descriptor is directly constructed from two ODBTC quantizers and the corresponding bitmap image without performing the decoding process. The color co-occurrence feature (CCF) derived from the ODBTC quantizers captures the color distribution and image contrast in block based manner, while the Bit Pattern Feature (BPF) characterizes image edges and visual patterns. The similarity between two images can be easily determined based on their CCF and BPF under a specific distance metric measurement. A metaheuristic algorithm, namely Particle Swarm Optimization (PSO), is employed to find the optimum similarity constants and improve the retrieval accuracy. Experimental results demonstrate that the proposed indexing method is superior to the former Block Truncation Coding (BTC) image retrieval system and the other existing methods. The ODBTC method offers an effective way to index an image in a content-based image retrieval system, and simultaneously it is able to compress an image efficiently. Thus, this system can be a very competitive candidate in image retrieval applications.

Improved Block Truncation Coding Using Optimized Dot Diffusion

Block truncation coding (BTC) has been considered a highly efficient compression technique for decades. However, its inherent artifacts, blocking effect and false contour, caused by low bit rate configuration are the key problems. To deal with these, an improved BTC, namely dot-diffused BTC (DDBTC), is proposed in this paper. Moreover, this method can provide excellent processing efficiency by exploiting the nature parallelism advantage of the dot diffusion, and excellent image quality can also be offered through co-optimizing the class matrix and diffused matrix of the dot diffusion. According to the experimental results, the proposed DDBTC is superior to the former error-diffused BTC in terms of various objective image quality assessment methods as well as processing efficiency. In addition, the DDBTC also shows a significant image quality improvement comparing with that of the former ordered-dither BTC.

Reversible data hiding in low complexity and high quality compression scheme

Nowadays, most multimedia is stored in compressed bit stream format to save the storage space or transmission time. This study proposes a novel technique for embedding flexible amounts of data in the bit map of the improved Ordered Dither Block Truncation Coding (ODBTC) image, where the ordered dithering is used to dither the quantized BTC image to avoid the annoying false contour and blocking effect inherently existed in BTC image. Moreover, the LUT strategy is also used to significantly reduce the complexity. The cell averaging and the second round of dithering are employed as the key steps in locating the embedded information bits. Experimental results demonstrate that an objective good quality image with flexible capacity and reasonable complexity is obtained. Moreover, the correct decoding rate of 100% is maintained, and the original host ODBTC image can also be reconstructed in the decoder when needed, which significantly boosts the flexibility in image quality control.

High efficiency ordered dither block truncation coding with dither array LUT and its scalable coding application

Block truncation coding is an efficient compression technique while offering good image quality. Nonetheless, the blocking effect inherent in BTC causes severe perceptual artifact in high compression ratio applications. In this study, the ordered dither block truncation coding (ODBTC) is proposed to solve this problem, where the dither array look up table (LUT) approach is proposed to significantly reduce the complexity of the BTC. Moreover, a novel progressive coding scheme is presented for efficiently displaying ODBTC images. The ODBTC utilizes the characteristic of the bit-interleaved bit map to determine the transmitting order and then progressively reconstructs the ODBTC image. In addition, the progressive ODBTC can provide higher compression ratio by toggling and entropy coding treatments. The experimental results demonstrate high-quality reconstructions while maintaining low transmitted bit rates.

Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach

Block Truncation Coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely Ordered Dither Block Truncation Coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array Look Up Table (LUT), which significantly reduces the complexity compared to the BTC.