Block Truncation Coding Research Articles

K-d Tree-Segmented Block Truncation Coding for Image Compression

Block truncation coding (BTC) is a class of image compression algorithms whose main technique is the partitioning of an image into pixel blocks that are then each encoded using a representative set of pixel values. It is commonly used because of its simplicity and low computational complexity. The Quadtree-segmented BTC (QTS-BTC), which utilizes a dynamic hierarchical segmentation technique, is among the most efficient in the BTC class. In this study, we propose a new BTC variant that introduces two ideas: (1) the use of a k -d tree for segmentation and (2) the use of a Mean Squared Error (MSE) threshold for dynamically determining the granularity of the blocks. We refer to this new BTC variant as the k -d Tree Segmented BTC (KTS-BTC), and we test this against some of the existing BTC variants by running the algorithms on a standard image compression dataset. The results show that the proposed variant yields low bit rates of the compressed images, even outperforming the state-of-the-art QTS-BTC, without a significant reduction in image quality as measured using the Peak Signal-to-Noise Ratio (PSNR). The utilization of k -d tree for image segmentation is further shown to have more impact than that of employing the MSE thresholding scheme as a block activity classifier.

Modified BTC Algorithm for Audio Signal Coding

This paper describes modification of a well-known image coding algorithm, named Block Truncation Coding (BTC) and its application in audio signal coding. BTC algorithm was originally d ...

Content based Video Retrieval Systems Performance based on Multiple Features and Multiple Frames using SVM

In this paper, Content Based Video Retrieval Systems performance is analysed and compared for three different types of feature vectors. These types of features are generated using three different algorithms; Block Truncation Coding (BTC) extended for colors, Kekre’s Fast Codebook Generation (KFCG) algorithm and Gabor filters. The feature vectors are extracted from multiple frames instead of using only key frames or all frames from the videos. The performance of each type of feature is analysed by comparing the results obtained by two different techniques; Euclidean Distance and Support Vector Machine (SVM). Although a significant number of researchers have expressed dissatisfaction to use image as a query for video retrieval systems, the techniques and features used here provide enhanced and higher retrieval results while using images from the videos. Apart from higher efficiency, complexity has also been reduced as it is not required to find key frames for all the shots. The system is evaluated using a database of 1000 videos consisting of 20 different categories. Performance achieved using BTC features calculated from color components is compared with that achieved using Gabor features and with KFCG features. These performances are compared again with the performances obtained from systems using SVM and the systems without using SVM.

Score Level Fusion Based Bimodal Biometric Identification Using Thepade's Sorted n-ary Block Truncation Coding with Variod Proportions of Iris and Palmprint Traits

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Multimodal biometrics such as palmprints and iris provide highly effective automatic mechanisms for use in personal identification and helps to minimize the system error rate. This paper presents a new method for extracting features in spatial domain from palmprints and iris. Thepade's Block Truncation Coding using level 2 is taken here to reduce the feature vector size. For improving accuracy in form of genuine acceptance rate(GAR)in multimodal biometric identification we take iris and palmprint together at matching score level. The test beds of 60 pairs of Iris and Palmprint samples of 10 person (6 per person of iris as well as Palmprint) are used as test bed for experimentation. In this Paper different color spaces are considered on iris images for improvement in genuine acceptance ratio (GAR). Experimental results consider different matching score proportion of Iris: Palmprint. Using propose techniques with Iris: Palmprint Score 15:1 using TSTBTC Level2 given better performance as indicated by higher GAR values than all other considers scores. RGB color spaces for multimodal fusion of Iris: Palmprint gives high GAR than all other color spaces like YCgCb, YIQ, YCbCr, YIQ, and KLUV.

Novel Video Content Summarization Using Thepade's Sorted n-ary Block Truncation coding

With the quick growth of multimedia technology, a huge amount of videos is available across the world. Length of these videos may be large; user may require only viewing the summary of video. In such cases, video content summarization is used. A video comprise of no. of frames. Video content summarization gives the concise form of the video. Key frame represent the main content of video. For video content summarization key frame extraction is mainly considered. This paper proposes novel method to extract key frames from video using Thepade's sorted n-ary block truncation coding. Here total five variations of TSBTC's has done. Out of these, Thepade's sorted pentnary (TSPBTC), performs better in all similarity measures. Canberra distance give's effective performance in all TSBTC's & L1 distance family delivers highest performance as compared to other family.

VLSI implementation of an ultra-low-cost and low-power image compressor for wireless camera networks

In this paper, a novel hardware-oriented color image compression algorithm based on digital halftoning and block truncation coding (BTC) techniques is proposed for very large-scale integration (VLSI) implementation. The proposed technique consists of a threshold generator, a bitmap generator, a BTC training module, a predictor, and a signed Golomb–Rice coding module. Because the wireless camera network is developed for perceiving, measuring, and collecting information from the environment, each camera sensing node should be designed to achieve low cost, small size, high compression rates, and low power consumption. Hence, a novel low-complexity, high-performance, transform-free, hardware-oriented color image compression algorithm based on digital halftoning and BTC is proposed. In order to achieve the performance of compressing images in real time, the proposed color image compression algorithm was realized by a VLSI technique. The VLSI architecture in this work contains only 8.1-k gate counts, and the core area is 81,000 μm2 synthesized using a TSMC 0.18-μm CMOS process. The operating frequency of this work is 100 MHz and consumes 2.91 mW, which is efficient to develop wireless electronic systems with the demand of color image compression in real time. Compared with JPEG- and JPEG-LS-based designs, this work reduces gate counts by at least 71.1 % and power consumption by 53 % and only requires a one-line-buffer memory.

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.

A novel hybrid image authentication scheme based on absolute moment block truncation coding

We propose a novel hybrid image authentication scheme to protect the integrity of images compressed by absolute moment block truncation coding (AMBTC). In this paper, we use a hybrid strategy to embed the authentication codes using AMBTC that at the same time improves the embedding efficiency. All blocks of a compressed image are classified into two groups, smooth and complex, which have different characteristics and may suit different embedding methods. For smooth blocks, the authentication code will be embedded into the bitmap (BM) of each block. For complex blocks, the authentication code will be embedded into quantization levels of each block according to a reference table. In contrast to the previous approach, the hybrid strategy allows for a high utilization of blocks that have high embedding efficiency for embedding, thus giving a significantly improved embedding capacity than the previous approach. The performance is evaluated experimentally to demonstrate that our proposed scheme provides improved image quality and is less vulnerable to tampering compared with existing methods.

Near-Aperiodic Dot-Diffused Block Truncation Coding

Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion.

Bi-stretch reversible data hiding algorithm for absolute moment block truncation coding compressed images

Steganography is one of the most important approaches for secure transmission by concealing secret data into a host image imperceptibly. To achieve a good tradeoff between the hiding capacity and image quality, more work needs to be further researched. In this paper, to obtain satisfactory results, a Bi-Stretch Hiding (BSH) algorithm for absolute moment block truncation coding (AMBTC)-compressed image is proposed. In the scheme, the AMBTC-compressed image is divided into non-overlapped blocks first, after that, four feasible cases are employed to embed secret data, which takes advantage of the characteristics of the coefficients of the AMBTC-compressed image and lead tiny distortion of the AMBTC-compressed image. The experimental results demonstrate that the proposed BSH scheme outperforms the other state-of-the-art compression data hiding methods.

Effective Image Retrieval System Using Dot-Diffused Block Truncation Coding Features

This paper presents a new approach to derive the image feature descriptor from the dot-diffused block truncation coding (DDBTC) compressed data stream. The image feature descriptor is simply constructed from two DDBTC representative color quantizers and its corresponding bitmap image. The color histogram feature (CHF) derived from two color quantizers represents the color distribution and image contrast, while the bit pattern feature (BPF) constructed from the bitmap image characterizes the image edges and textural information. The similarity between two images can be easily measured from their CHF and BPF values using a specific distance metric computation. Experimental results demonstrate the superiority of the proposed feature descriptor compared to the former existing schemes in image retrieval task under natural and textural images. The DDBTC method compresses an image efficiently, and at the same time, its corresponding compressed data stream can provide an effective feature descriptor for performing image retrieval and classification. Consequently, the proposed scheme can be considered as an effective candidate for real-time image retrieval applications.

Dot Diffusion Block Truncation Coding for Satellite Image Retrieval

In present survey it is noticed that the profound interest in research and study of retrieval of satellite images and Image Retrieval on Content Based is grown hugely .Thus, to build the semantic error which is a huge challenge. Also, it prevents wide hosting of image on content based search engines which is now the necessity of CBIR technique. Mostly image search engine depend on human generated data as input query namely tags or annotations. The images get stored in database according to the annotations or tags assigned to them. Thus, tags or annotations are used as inputs by search engines .Thus, to overcome the limitation of annotations based image retrieval an Improved Block Truncation Coding is used which is based on Texture and color features of an image. In this paper, Improved Block Truncation Coding technique is used to fill the semantic gap. Then the similarity features type of search is carried to find different query regions namely desert, coastal, forest, metro from the satellite images. The retrieval technique can be practically applied and compared using different similarity methods .The technique of Improved Block Truncation Coding is described which is one of the most proficient methods used in retrieval of satellite images. Though, in traditional BTC certain redundancy like false contour, inherent artifacts are observed. In order to deal with this an improved BTC with dotdiffusion is applied to the system. Here, a Dot Diffusion is added for retrieving the most relevant images that best match to the query image whereas Dot Diffusion is used to give best quality image with good clearity and save memory need to store in database.

패턴을 이용한 블록 절단 부호화 기반의 영상 압축

Block Truncation Coding, or BTC, is a type of lossy image compression technique for grayscale images. It divides the original images into blocks and then reduces the number of grey levels in each block to compute the mean and standard deviation. BTC has also been adapted to video compression. Another variation of BTC is Absolute Moment Block Truncation Coding. AMBTC is computationally simpler than BTC. In this paper, we proposed new image compression method based on BTC, which is applied patterns to improve compression rate and image quality. This method make two codebooks to extract 36 and 64 patterns from the highest frequency patterns in BTC. When you are compressing an image, you compare many block patterns to that of codebook and use to compress indexes of identical patterns. We experiment our proposed scheme with 36 patterns and the experimental results showed the compression rate of 1.37 bpp. In this paper, our proposed scheme showed higher compression rate rather than that of BTC. In experiment, we used standard images for the performance evaluation.

Novel image authentication scheme with fine image quality for BTC-based compressed images

A novel image authentication scheme which can protect the image integrity of the compressed images for block truncation coding (BTC) is proposed in this paper. In the proposed scheme, the authentication codes are embedded into the the quatization levels of each BTC-compressed image block by using reference matrix--- RMB. The size of the authentication codes can be decided according to the user's requirement by adjusting the value of B in Reference Matrix. The experimental results demonstrate that the proposed method outperforms previous approaches in image quality of the embedded image and high detecting accuracy.

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

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 a bitmap image, 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. 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.

User-friendly secret image sharing scheme with verification ability based on block truncation coding and error diffusion

Conventional secret image sharing schemes, which are constructed based on Shamir’s method, often suffer from random-liked shares, lossy reconstruction and high computation complexity. In addition, their generated shares are generally in original image format which may lead to more storage and suspicion from invaders. In this paper, we propose a user-friendly secret image sharing scheme based on block truncation coding (BTC) and error diffusion, where meaningful shares can be directly generated without any extra process. The meaningful shares by the proposed scheme are in BTC-compressed format which can reduce the capacity of transfer and storage. In the reconstructing phase, the secret image can be losslessly reconstructed by performing XOR operations on bit planes of sufficient BTC-compressed shares. Further, the proposed scheme provides extra verification ability to identify cheaters and check false shares. Theoretical analysis and simulation results demonstrate the feasibility of the proposed scheme.

Adaptive block truncation coding technique using edge-based quantization approach

In this paper a new approach of edge-based quantization for the compression of gray scale images using an Adaptive Block Truncation Coding technique (ABTC-EQ) is proposed, to improve the compression ratio (CR) with high picture quality. Quantization is done based on the edge information contained in each block of pixels of the image. Conventional BTC method retains the visual quality of the reconstructed image but it shows some artifacts near the edges. In conventional BTC and variants, same quantization is done for all pixel values with different block sizes so that CR is static for images with a fixed block size. But in the case of proposed method since the quantization is done based on the edge information, CR become dynamic and consequently achieves better visual quality with better CR. The experimental analysis based on subjective and quantitative analysis proved that the proposed method outperforms other BTC variants.

Content Based Image Classification with Thepade's Static and Dynamic Ternary Block Truncation Coding

Image data has been considered as a vital source of information with far-fetched growth of Information Technology. World Wide Web has facilitated easy and round the clock access of data. Archiving of image data in good proportion has been made possible with high capacity storage devices and communication links. Time and efficiency have been considered as most important factors for information recognition from these datasets. The huge numbers of information databases have diverse categories of image data. Limited number of major categories can be formed based on the contents of the images with the help of image classification. The authors have proposed two novel techniques of feature extraction in this work and have compared the same with the existing techniques of feature extraction for classification results. The proposed techniques have exhibited higher performance efficiency compared to the state-of-the art techniques and have principally contributed to boost up classification performance.

A High-Quality Image Authentication Scheme for AMBTC-compressed Images

In this paper, we present a high-quality image authentication scheme based on absolute moment block truncation coding. In the proposed scheme, we use the parity of the bitmap (BM) to generate the authentication code for each compressed image block. Data hiding is used to authenticate whether the content has been altered or not. For image authentication, we embed the authentication code to quantization levels of each image block compressed by absolute moment block truncation coding (AMBTC) which will be altered when the host image is manipulated. The embedding position is generated by a pseudo-random number generator for security concerned. Besides, to improve the detection ability we use a hierarchical structure to ensure the accuracy of tamper localization. A watermarked image can be precisely inspected whether it has been tampered intentionally or incautiously by checking the extracted watermark. Experimental results demonstrated that the proposed scheme achieved high-quality embedded images and good detection accuracy, with stable performance and high expansibility. Performance comparisons with other block-based data hiding schemes are provided to demonstrate the superiority of the proposed scheme.

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.