Modified Block Truncation Coding Research Articles

Comparative Analysis on Image Compression Algorithm Based on Block Truncation Coding

Block Truncation Coding is a quick and effective method for compressing images, because it employs a non-parametric one-bit or two-bit quantization procedure that adapts to the features of the image. This paper describes and compares the performance of BTC and BTC based techniques such as Enhanced Block Truncation Coding (EBTC), Modified Block Truncation Coding (MBTC), and Adaptive Block Truncation Coding – Edge Quantization (ABTC – EQ). These techniques were applied on the general images and their performance were measured using the metrics such as Peak Signal Noise Ratio (PSNR), Structured Similarity Index Measure (SSIM), Weighted Peak Signal Noise Ratio (WPSNR), Feature Similarity Index Measure (FSIM) and Compression Ratio (CR).

Joint Data Hiding and Compression Scheme Based on Modified BTC and Image Inpainting

Seamlessly integrating image compression technique and secret data hiding technique into a single procedure for security and efficient data transmission is a novel research issue in modern, decentralized digital communication environments. The first joint data hiding and compression (JDHC) scheme on block truncation coding (BTC) compression domain is presented in this paper. In the compressing and embedding procedure, for the complex blocks, modified block truncation coding (MBTC) is utilized to embed secret data and compress blocks simultaneously, while further controlling the visual distortion that is caused during data embedding. For the smooth blocks, according to the current embedding bit, either image inpainting or block search order coding (BSOC) is used to embed secret data and compress blocks simultaneously with maintaining acceptable compression performance. According to the image compression codes that are provided as output, image decompression and secret bits extraction procedures can be conducted simultaneously. Experimental results indicate that outstanding compression bit rate can be achieved by the proposed JDHC scheme with satisfactory visual quality and hiding capacity.

Clustering-based compression connected to cloud databases in telemedicine and long-term care applications

A novel clustering-based compression work connected to cloud databases is proposed for the applications of telemedicine and long-term care in this study, where the goal is to enhance information transfer rate and storage capacity to further improve communication between medical staffs and patients in long-term care and telemedicine. The proposed system mainly involves three-dimensional histogram competitive Hopfield neural network (CHNN) clustering, regionalization, and modified block truncation coding (BTC). Three-dimensional histogram CHNN clustering and regionalization are proposed to achieve better clustering accuracy within three-dimensional spaces and simultaneously overcome the problems of fluctuating initial values of clustering. Modified BTC is also proposed to analyze clustering regions with different compression rates according to their importance in order to greatly preserve important image feature information under the condition of smaller image sizes. The experimental results indicate that the proposed system is adaptive and performs better than several previous methods. It is also suggested being suitable for the applications of telemedicine and long-term care connected to cloud databases.

Color &Texture Based Image Retrieval using Fusion of Modified Block Truncation Coding (MBTC) and Kekre Transform Patterns

Content Based Image Retrieval is an interesting topic of research. This paper is about image content-based image search. Specifically, it is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of image content-based search engines. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this paper a CBIR is proposed based on color & texture based search. Modified Block Truncation Coding (MBTC) is used for color information retrieval. To extract texture information we are using pattern generated by transforms, currently we are considering Kekre Transform. The feature vector is generated by fusion of above mentioned techniques.

Feature Extraction using Overlap Blocks for Content based Image Retrieval

The paper presents an extension of content based image retrieval (CBIR) techniques based on Overlap Modified Block Truncation Coding (BTC) and Overlap Gabor Magnitude. Modified Block truncation coding based features is one of the CBIR methods proposed using color features of image. The approach basically considers red, green and blue planes of an image to compute feature vector. This MBTC based CBIR can be extended as Overlap Modified BTC for performance improvement in image retrieval. The Overlap Gabor Magnitude technique is used for texture features of image. The CBIR techniques like MBTC, Gabor and Fusion of MBTC and Gabor are further modified using overlap blocs technique to analyze and compare their performances. The proposed CBIR technique is implemented on a database having 1000 images spread across 11 categories and COIL image database having 1080 images spread across 15 categories. For each proposed CBIR technique 55 queries (5 per category) are fired on the database and net average precision and recall are computed. The results have shown performance improvement (higher precision and recall values) with Overlap (Fusion technique , Gabor Magnitude and Modified BTC compared to Non-Overlap (Fusion technique , Gabor Magnitude and Modified BTC ).In all Overlap technique based CBIR system gives best performance.. General Terms Algorithm, Performance

Ratio‐modified block truncation coding algorithms for reduced bitrates

Block truncation coding (BTC) technique is a simple and fast image compression algorithm since complicated transforms are not used. The principle used in BTC algorithm is to use two‐level quantiser that adapts to local properties of the image while preserving the first‐ or first‐ and second‐order statistical moments. The parameters transmitter or stored in the BTC algorithm are statistical moments and bitplane yielding good quality images at a bitrate of 2 bits per pixel (bpp). In this paper, two algorithms for modified BTC (MBTC) are proposed for reducing the bitrate below 2 bpp. The principal used in the proposed algorithms is to use the ratio of moments which is a smaller value when compared to absolute moments. The ratio values are then entropy coded. The bitplane is also coded to remove the correlation among the bits. The proposed algorithms are compared with MBTC and the algorithms obtained by combining JPEG standard with MBTC in terms of bitrate, peak signal‐to‐noise ratio (PSNR) and subjective quality. It is found that the reconstructed images obtained using the proposed algorithms yield better results.

Image compression using modified block truncation coding algorithm

This paper presents a modified block truncation coding (BTC) algorithm for image compression. Similar blocks in an image are merged into a cluster and represented with the cluster center. Each cluster center is then encoded with a vision block truncation coding (VBTC) algorithm which uses a small set of predefined binary edge patterns to approximate the bit plane of the cluster center. Experimental results show that the computation efficiency of the proposed algorithm is significantly improved when compared to the original BTC.

Put absolute moment block truncation coding in perspective

The purpose of this letter is to point out that the Udpikar and Raina (1987) modified block truncation coding (BTC) has been misleading researchers as a different BTC scheme in multiple publications for years. In fact, their algorithm is exactly identical to the second version of the Lema and Mitchell (1984) absolute moment block truncation coding (AMBTC). Mathematical proof is provided.