Block Truncation Coding

ABSTRACT: Biomedical images are important factor in health care for diagnosis of disease. As medical imaging facilities are moving towards film less imaging technology, digital image processing techniques plays an important role. Image compression technique is an important multimedia application to effectively store and transmit data at lower bandwidth. BTC based algorithm is designed to compress the biomedical x-ray image. A new application of block truncation coding (BTC) is presented to compress gray scale X-ray images with square and rectangular non- overlapping truncation matrix. The compressed image characteristics parameters such as signal to noise ratio (SNR), peak signal to noise ratio (PSNR), root mean square error (RMSE), and mean absolute error (MAE) with respect to original images are computed. Experimental results are presented which demonstrate that at lower value of truncation matrix, the compression rate is low with image characteristic parameters close to the original image values. As the size of truncation matrix is increased, higher compression of the x-ray image is obtained. Comparative analysis between square truncation matrix and rectangular matrix is also carried out.

VIII. CONCLUSION With Thepade's Ternary Block Truncation Coding (TTBTC) and Thepade's Sorted Ternary Block Truncation Coding (TSTBTC), the proposed method helps to upgrade the performance of retrieval of videos from the database using color contents. The proposed Thepade's ternary BTC has more bands than binary BTC so finer details of video pixels are extracted. Better performance is observed in case of TSTBC which have shown better results than TTBTC and BTC. The best performance is obtained in case of KLUV color space with TSTBTC based CBVR followed by YIQ color space in case of TSTBTC based CBVR.

Head of Dept, Department of Computer Science & Engineering, All Saints College of Technology, Bhopal, India ABSTRACT: In the present era of multimedia, the requirement of image/video storage and transmission for video conferencing, image and video retrieval, video playback, etc. are increasing exponentially. As a result, the need for better compression technology is always in demand. Modern applications, in addition to high compression ratio, also demand for efficient encoding and decoding processes, so that computational constraint of many real-time applications is satisfied.Two widely used spatial domain compression techniques are block truncation coding (BTC) and vector quantization (VQ). BTC method results in good quality image with high bit-rate, while the VQ is well known for low bit-rate but produces poor quality images. In further work of this paper is BTC-PF includes BTC algorithm as well as vector quantization method for purpose of pattern fitting for gray and color image.

N. J. Satheesh Kumar 1 , Dr. B. Ramakrishnan 2 1 Research Scholar, 2 Associate Professor, Department of Computer Science and Research centre, S.T.Hindu College, Nagercoil, Tamilnadu, India. Abstract: This paper proposes a lossless data embedding technique for Block Truncation Coding (BTC) packed together images based on prediction and bit shifting techniques. We used BTC is easy to implement, and requires less CPU cost, it has stimulate widely attention in applications where real-time processing is demanded. Advances in computer method for mass data storage and digital processing have cemented the way for implementing advanced data compression techniques to get better the effectiveness of transmission and storage of images. Bit rate is the principal parameter of a compression technique because it measures and improves the efficiency and the low CPU utilization. The picture has to translate with bilateral shifts bitwise operation more bit patterns. The new alteration is like to the sub band decomposition but can be computed with right shift bit and left shift operations. Then calculation the number of bits required to represent the transformed image is kept diminutive through careful compression. The trial results reveal that the proposed system provides good image quality of the compressed implanted image.

Dr. Ghadah Al-Khafaji Baghdad University/Collage of Science/Computer Science Department/Iraq Hgkta2012@yahoo.com Abstract: In this paper, an interpolation technique of nearest neighbour base is proposed along with absolute block truncation coding that exploited the spatial domain of low resolution image, and then reversely reconstruct the up layers hierarchically. The results showed the superior performance of compression ratio and image quality compared to traditional absolute block truncation coding.

Jayamol Mathews et al. [2], with the emerging multimedia technology, image data has been generated at high volume. It is thus important to reduce the image file sizes for storage and effective communication. Block Truncation Coding (BTC) is a lossy image compression technique which uses moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image with good compression ratio, it shows some artifacts like staircase effect, raggedness, etc. near the edges. A set of advanced BTC variants reported in literature were studied and it was found that though the compression efficiency is good, the quality of the image has to be improved. A modified Block Truncation Coding using max-min quantizer (MBTC) is proposed in this paper to overcome the above mentioned drawbacks. In the conventional BTC, quantization is done based on the mean and standard deviation of the pixel values in each block. In the proposed method, instead of using the mean and standard deviation, an average value of the maximum, minimum and mean of the blocks of pixels is taken as the threshold for quantization. Experimental analysis shows an improvement in the visual quality of the reconstructed image by reducing the mean square error between the original and the reconstructed image. Since this method involves less number of simple computations, the time taken by this algorithm is also very less when compared with BTC.

Image classification process requires interaction with the image data in terms of features which strongly relates to the inherent properties of the image itself. The term „feature‟ in this perspective may be considered as color, shape, texture of an image. Averaging and Histogram techniques are used to realize the color facet of an image [3,4,5]. Texture can be obtained by using transforms [6,7] or vector quantization [8,9]. Shape aspects are achieved with gradient operator or morphological operator[10]. Earlier approaches have studied K-means clustering using Block Truncation Coding (BTC) and color moments to classify images into various categories [11]. Only RGB color space has been considered in previous works and performance comparison of classification with various color spaces is not performed.

Abstrak Kaedah pengesahihan imej semakin banyak menerima tumpuan disebabkan kepentingannya kepada kegunaan aplikasi multimedia. Selain itu penghantaran Imej digital semakin banyak dilakukan melalui saluran komunikasi yang terdedah dan rentan seperti Internet. Melindungi produk digital menjadi suatu isu penting bagi memastikan transmisi yang selamat. Algortima sedia ada tidak dapat menjamin keselamatan kandungan ataupun pengesahan pemilikan produk digital tersebut Penyiratan tanda air atau penyembunyian data dalam imej digunakan untuk mengenalpasti pemilik sesuatu imej. Block Truncation Coding (BTC) adalah satu daripada kegunaan masa nyata yang efektif bagi menyembunyikan data. Kaedah tanda air yang berasaskan BTC tidak dapat mengeksploitasi persepsi visual terhadap imej hadapan dan tidak mengandungi kadar penyiratan data yang tinggi Bagi mengeksploitasi persepsi visual dan penyiratan data yang tinggi, satu skema penyembunyian data yang dipertingkat berasaskan BTC dicadangkan. Dalam kaedah yang dicadangkan, penggantian tesktur sub blok dan Least Significant Bits (LSB) iaitu yang mempunyai nilai min lebih tinggi dan nilai min lebih rendah digabungkan bagi meningkatkan prestasi skima BTC. Ia dapat mengimbangi diantara keperluan kadar penyiratan (kapasiti) dan juga kualiti. Sensitiviti tekstur dieksploitasi untuk membezakan blok imej atau blok sub yang licin atau kompleks.

()() (8) 2.3 Quadtree Segmented Block Truncation Coding The quadtree segmented BTC is a hierarchical segmentation technique that partitions an image into variable-sized square blocks. During the quadtree segmentation process, a series of binary decisions with respect to different threshold values is made. A block type classifier is employed to determine the block activity, and it is usually based on a statistical information [11]. If a block is inactive (e.g., low variance) the segmentation of the given block is terminated, otherwise the block is further divided into four sub-blocks.

Jayamol Mathews et al. [2], with the emerging multimedia technology, image data has been generated at high volume. It is thus important to reduce the image file sizes for storage and effective communication. Block Truncation Coding (BTC) is a lossy image compression technique which uses moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image with good compression ratio, it shows some artifacts like staircase effect, raggedness, etc. near the edges. A set of advanced BTC variants reported in literature were studied and it was found that though the compression efficiency is good, the quality of the image has to be improved. A modified Block Truncation Coding using max-min quantizer (MBTC) is proposed in this paper to overcome the above mentioned drawbacks. In the conventional BTC, quantization is done based on the mean and standard deviation of the pixel values in each block. In the proposed method, instead of using the mean and standard deviation, an average value of the maximum, minimum and mean of the blocks of pixels is taken as the threshold for quantization. Experimental analysis shows an improvement in the visual quality of the reconstructed image by reducing the mean square error between the original and the reconstructed image. Since this method involves less number of simple computations, the time taken by this algorithm is also very less when compared with BTC.

Block Truncation Coding is one of the easy and efficient techniques for lossy image compression. In this paper, we have proposed few methods to enhance the existing Interpolative and Lookup procedures to reduce the bitrate and to improve the PSNR obtained with the Block Truncation Coding (BTC) based image compression methods. Of the existing BTC based methods, we have implemented the Minimum Mean Square Error (MMSE) method which gives better PSNR (quality of the reconstructed images) value to generate the bitplane and statistical moments. The size of the bitplane is reduced using Interpolation and Lookup procedures with noticeable degradation in PSNR value. This degradation is minimized using the proposed methods. Hence the proposed methods give less bitrate and better PSNR, when compared to the existing methods.

ABSTRACT: In the present era of multimedia, the requirement of image/video storage and transmission for video conferencing, image and video retrieval, video playback, etc. are increasing exponentially. As a result, the need for better compression technology is always in demand. Modern applications, in addition to high compression ratio, also demand for efficient encoding and decoding processes, so that computational constraint of many real-time applications is satisfied.Two widely used spatial domain compression techniques are block truncation coding (BTC) and vector quantization (VQ). BTC method results in good quality image with high bit-rate, while the VQ is well known for low bit-rate but produces poor quality images. In further work of this paper is multi-level BTC includes BTC algorithm as well as vector quantization method for purpose of multi-leveltechnique for gray and color image.

Abstract:This paper presents a new approach for compression and Retrieval. To index color images,features are extracted from the EDBTC it stands for Error Diffusion Block Truncation Coding. The EDBTC compression produces a bitmap image and two color quantizers, which are processed using vector quantization (VQ) to generate the image feature descriptor.Bitpattern Histogram Feature (BHF) and Color pattern Histogram Feature (CHF) are computed. The CHF is calculated from VQ-indexed color quantizer and BHF is calculated from VQ-indexed bitmap image.Based on these features, similarity is measuredbetween a query image and database images. Finally, image retrieval system returns a set of images to the user as output with a specific similarity criterion, such as texture and color similarities.Thus, proposed method EDBTC is examined with good capability for image compression and also it offers an effective way to index images for the image retrieval.

M Tech. Scholar, Dept. of Electronics and Communication, Bansal Institute of Science and Technology, Bhopal, India Professor & HOD, Dept. of Electronics and Communication, Bansal Institute of Science and Technology, Bhopal, India ABSTRACT: In the present era of multimedia, the requirement of image/video storage and transmission for video conferencing, image and video retrieval, video playback, etc. are increasing exponentially. As a result, the need for better compression technology is always in demand. The limited bandwidth of internet also asks for transmission of desired objects only. Modern applications, in addition to high compression ratio, also demand for efficient encoding and decoding processes, so that computational constraint of many real-time applications is satisfied. In this paper, first of all, a novel gray scale image compression method (BTC-PF) based on block truncation coding (BTC) and vector quantization (VQ) and second method for the compression of digitalimages using hybrid compression method based on BlockTruncation Coding (BTC). Since these method involves lessnumber of simple computations, the time taken by thisalgorithm is also very less when compared with BTC.

II. IMAGE COMPRESSION All the image compression methods work based on the assumption that there exists correlation among the neighboring pixels in the input image. Compression can be achieved by utilizing the correlation. Vector Quantization (VQ) [Gersho and Gray, 1992], Block Truncation Coding (BTC) [Delp and Mitchell, 1979], JPEG [Rao and Yip, 1990; Pennebaker et al., 1992; Andrew, 1994] and JPEG2000 [Skodras et al., 2000; Skodras et al., 2001] are the popular lossy image compression techniques. Unlike JPEG and JPEG2000, the VQ and BTC based methods are very popular in terms of lower implementation complexity. Vector Quantization technique is composed of two components: an encoder and a decoder. Both the encoder and the decoder have the same codebook. After the encoder takes an input vector, it makes a full search in the codebook to find the closest matching codeword with a minimum Euclidean distance to the input vector. Once the closest codeword is found, the index of that codeword is output for storage or transmission. Since the decoder has a same codebook as the encoder, it is able to look up the codebook for each incoming index and retrieve the corresponding codeword. Compression is achieved because only the index sequence needs to be stored or transmitted. Block Truncation Coding was introduced by Delp and Mitchell in 1979. It is a simple and lossy image compression technique. BTC has the advantage of being easy to implement compared to any other techniques. The BTC method preserves the block mean and the block standard deviation of the pixels of the images before encoding and after decoding. In the encoding procedure of BTC the image is first partitioned into a set of non-overlapping blocks, and hen the first two statistical moments and the bit plane are computed. In the decoder, each of the encoded image blocks is reconstructed using the bit.

A. Absolute moment block truncation coding In AMBTC-compression scheme, an image is first decomposed into some non-overlapping k × k-sized sub-blocks, such as 2 × 2, 3 × 3 or 4 × 4 and so on. The block size, k is often set to be 4 in the conventional AMBTC encoding scheme. For sub- blocks, sample mean pixel value, 𝑥 and sample absolute central moment, ∝ are described in below equation [7].

The Hybrid technique that combines the Wavelet Transformation and Block Truncation Coding is a new framework proposed for fingerprint image compression. The proposed technique includes image enhancement system, forward DWT, encoding system, decoding system and inverse DWT. Image enhancement techniques like histogram equalization and wiener filtering has been performed on the image before Wavelet transformation so that the noisy image can also be retrieved fast. Wavelet based compression are largely used due to the competitive compression ratios that can be achieved at high quality without much blocking artefacts. The BTC converts a gray level image into a binary image with a predefined threshold. The coded bits are written into separate text file as sequence of symbol pairs (run, value) using run- length coding. Experiments on an image database of grayscale JPEG (uncompressed) images shows that the proposed technique performs well in compression and decompression. The Quality metrics used are CR, SNR, MSE and PSNR. General terms

communication technologies, demand for data compression is increasing rapidly. Techniques for achieving image data compression can be divided into two basic approaches: spatial coding and Transform coding. This research paper presents a proposed method for the compression of digital images using hybrid compression method based on Block Truncation Coding (BTC) and discrete cosine Transform (DCT). The objective of this hybrid approach is to achieve higher compression ratio by applying BTC and DCT. Several gray scale test images are used to evaluate the coding efficiency and performance of the hybrid method and compared with the BTC and DCT respectively. It is generally shown that the proposed method gives better results.

Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing ABSTRACT Image classification demands major attention with increasing volume of available image data. The paper has shown performance boosting of image classification after associating Bit Plane Slicing with Block Truncation Coding (BTC) for feature extraction. Here more significant bit planes were considered for extraction of feature vectors. RGB color space was considered to carry out the experimentation. A database of 900 images was used for evaluation purpose.

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.