SPIHT Research Articles

Improved pharma education system in the field of medical images using compression techniques

The pharmaceutical education has been gaining more and more popularity in the past one and half decades. The pharma people are focusing on Remote Education for students. The medical images and reports pertaining to each individual should be stored in the database for any future references. Medical images generally occupy substantial amount of space. In this research article, we have emphasized how information can be stored within the stipulated space and how the space occupied can be reduced. The compression techniques applied are discrete cosine transform followed by a singular value decomposition method which is again cascaded by set partitioning in hierarchical trees, which will reduce 23.67% of the size of the original image.The size reduction will help in storing more number of images in the database thereby giving the Remote Education students a chance to observe and learn. The proposed technique when compared with other existing methods gives better quality of image that is reconstructed than the other methods in terms of PSNR value.

New Modified SPIHT Algorithm for Data Compression System

This paper presents a new modified set partitioning in hierarchical trees (MSPIHT) algorithm, which is used to generate absolute data values, and to convert decimal numbers into binary as a bit-plane. Then a new encoding method is used to process electrocardiography (ECG) data compression systems. This new ECG compression system uses one-dimensional reversal round-off non-recursive discrete periodized wavelet transform (1D RRO-NRDPWT), a quantization scheme, the new MSPIHT encoding, MSPIHT decoding, an inverse quantization scheme, and an inverse one-dimensional RRO-NRDPWT to reconstruct ECG signals. Experiment results indicate that the proposed new MSPIHT algorithm can ensure that reconstructed and original signals are approximately the same, with low complexity coding. It can also reduce the ECG signal to very few bits, enable the development of smaller devices, reduce the required PC memory capacity, improve performance speed, obtain a high compression ratio and reduce the percentage root mean square difference values. Therefore, this new approach will enhance wireless transmission for improved medical care.

A Low-Cost Hardware Design of a 1-D SPIHT Algorithm for Video Display Systems

This paper presents low-cost compressors for video display systems, which compress video data to reduce the size of the frame memory in a display panel in a television or a smartphone. The hardware solution exploits the discrete wavelet transform (DWT) and 1-D set partitioning in hierarchical trees (SPIHT) to support the raster-scan processing order in a display system and meet the fixed target compression ratio (CR). However, the compression efficiency and hardware cost often increase in proportion to the block size and thus, designing a compressor with low cost but high fidelity is challenging. This paper proposes a generic partitioned SPIHT algorithm, which achieves a low-cost design for mobile devices by allowing various sizes of partitioned sub-blocks. By taking advantage of DWT decomposition properties, the proposed algorithm partitions a coding block into sub-blocks, each of which can be processed independently. The independent coding operations among multiple sub-blocks allow the size of the hardware buffer to be decreased, by storing the temporary results only for an individual sub-block. However, the proposed sub-block coding slightly decreases the compression efficiency when each sub-block is compressed with the same target CR. To reduce the drop-off in the compression efficiency, the boundary-pixel handling in DWT is exploited and the proposed partitioned SPIHT method adjusts the target CR for each sub-block depending on the potential quality loss. When compared with the previous 1-D design, experimental results show that the hardware gate count and internal memory are reduced by 59.34% and 75%, respectively. Furthermore, the proposed boundary handling and bit-allocation schemes mitigate the PSNR degradation due to the sub-block coding by 1.11 dB.

Distributed Source Coding of Hyperspectral Images Based on Three-Dimensional Wavelet

To reduce the possibility of poor efficiency and weak anti-error capability while encoding and transmitting hyperspectral images, we present a distributed source coding scheme for hyperspectral images based on three-dimensional (3D) set partitioning in hierarchical trees (SPIHT). First, the 3D wavelet transform is performed on the hyperspectral image. Thereafter, the low frequency section is regarded as the Key frame and the high frequency section as the Wyner–Ziv frame to enable independent SPIHT coding through different transmission channels. The Wyner–Ziv encoder uses Turbo channel coding to create high frequency information that reflects the details of the image with better anti-error capacity, while the low frequency information shows the main energy of the image. In this study, we used SPIHT coding to acquire a bit stream with quality scalability. Results show that the proposed scheme is more efficient during coding, while at the same time providing improved anti-error capability and quality scalability of the bit stream.

A new approach for the image compression to the medical images using PCA-SPIHT

In this paper, Principal Component Analysis (PCA) with Set Partitioning in Hierarchical Trees (SPIHT) propounds to accomplish an image compression. A lossy technique is introduced through the PCA which is followed by SPIHT to enhance the compression performance. The Peak Signal to Noise Ratio (PSNR) value of the reconstructed image acquired from the PCA methods is not found to be sufficient which can be further improved by another method called SPIHT. In this paper, a hybrid compression model is constructed to accomplish the benefits of both PCA and SPIHT. In the bio-medical research, compression becomes necessary due to transfer of patients images from one group of experts to other experts group. So that, the proposed image compression technique is useful for conserving the storage space needed for healthcare systems. Finally, it is concluded that the proposed PCA-SPIHT performs better than other recent state-of-the-art techniques with acceptable loss of image quality.

Satellite Image Compression Based On SPIHT Algorithm

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

Satellite Image Compression Based On SPIHT Algorithm

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications.

In this paper, a speed and power-efficient set partitioning in hierarchical trees (SPIHT) design is introduced for one-dimensional (1-D) wavelet-based electrocardiography (ECG) compression systems with quality guarantee. To achieve real-time and low-power design objectives toward wearable quality-on-demand (QoD) ECG applications, we first propose a coding-time- and computation-efficient SPIHT algorithm using various types of coding status register files to overcome the disadvantages of low coding speeds and complicated hardware architectures characterizing prior SPIHT algorithms resulting from the necessity of dynamic computation and arrangement in the sorting and refinement processing phase. Second, a highly pipelined and power-efficient very large scale integration (VLSI) architecture is developed to implement a high-performance and low-power SPIHT design based on the proposed algorithm. The final simulation results demonstrate that our proposed algorithm can speed up the average coding time 1.52 to 2.74 times compared to prior work with an identical compression ratio for an 11-level $1024\times 1\,1-{\rm{D}}$ discrete wavelet transform at diverse target percentage root-mean-square differences (PRDT) on various MIT-BIH arrhythmia datasets. Applied to wearable wavelet-based QoD ECG applications, our proposed VLSI architecture attains a working frequency of 740MHz and consumes an average of $\text{23}\ \mu {\text{W}}$ of power with Taiwan Semiconductor Manufacturing Company 90-nm CMOS technology, which shows the effectiveness of speed and power over the state-of-the-art designs.

Rapid and coding-efficient SPIHT algorithm for wavelet-based ECG data compression

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient coding scheme widely used in wavelet-based electrocardiography (ECG) data compression systems. Traditional SPIHT schemes using three lists and a complex sorting process are unsuitable for wearable device design due to their time-consuming nature and low storage efficiency. In this paper, based on a bit-plane representation of quantized wavelet coefficients, a modified SPIHT algorithm is proposed for fast lossless coding. The bit plane is first represented by a tree data structure consisting of two types of primitive trees. The number of primitives is relative to the number of input sampled data. The coding process of the bit-plane data can be regarded as a synthesis that assembles the primitives sequentially based on various decision rules. The assembly rules are further simplified into logic-level decisions in terms of a flag scheme. Using the MIT-BIH arrhythmia database, the experimental results show that the proposed algorithm can achieve an up to 64.35% coding time reduction in comparison with the traditional SPIHT algorithm at a cost of a 0.28% bit-rate increase. The proposed algorithm is simple, regular, and modular.

Image compression algorithm of floral canopy based on mask hybrid coding for ROI

To achieve high-quality image compression of a floral canopy, a region of interest (ROI) mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees (ROI-SPIHT) coding. The embedded zerotree wavelet (EZW) coding was conducted for the background (BG) region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding. Through the weighing factor r of the ROI coding amount, the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded, truncatable bit streams. Despite the location of truncation, the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI. The results indicated that under the same bit rate, the larger the r value is, the larger the peak-signal-to-noise ratio (PSNR) for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image. In the range of 0.07-1.09 bpp, the PSNR of the ROI reconstructed image was 42.65% higher on average than that of the BG reconstructed image, 43.95% higher on average than that of the composite image of the ROI and BG (ALL), and 16.84% higher on average than that of the standard SPIHT reconstructed image. Additionally, the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG, ALL, and standard SPIHT reconstructed images. The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image, indicating that the image compression algorithm based on the mask hybrid coding for ROI (ROI-MHC) is capable of improving the reconstruction quality of an ROI image. When the weighing factor r is a fixed value, as the proportion of ROI (a) increases, the quality of ROI image reconstruction gradually decreases. Therefore, upon the application of the ROI-MHC image compression algorithm, high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a. Under the same bit rate, the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities. Keywords: floral canopy, ROI, mask hybrid coding, image compression, algorithm, wavelet transform DOI: 10.25165/j.ijabe.20171005.2772 Citation: Sun G X, Wang X C, Ding Y Q, Li Y H, Zhang B H, Li Y B, et al. Image compression algorithm of floral canopy based on mask hybrid coding for ROI. Int J Agric & Biol Eng, 2017; 10(5): 166–176.

QUALITY ASSESSMENT OF MEDICAL IMAGE COMPRESSED BY CONTOURLET QUINCUNX AND SPIHT CODING

In the field of medical diagnostics, interested parties have resorted increasingly to medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality, but the quality of the image is itself dependent on a number of factors including primarily the processing that an image must undergo to enhance its quality. The quality evaluation of compressed image is necessary to judge the performance of a compression method. This paper introduces an algorithm for medical image compression based on hybrid nonsubsampled contourlet (NSCT) and quincunx wavelet transforms (QWT) coupled with set partitioning in hierarchical trees (SPIHT) coding algorithm, of which we present the objective measurements (PSNR, EDGE, WPSNR, MSSIM, VIF, and WSNR) in order to evaluate the quality of the image.

Chaotic Interleaving for the Transmission of Compressed Video Frames with Self-Embedded Digital Signatures

This paper presents an efficient video compression and transmission scheme based on the Set Partitioning In Hierarchical Trees (SPIHT) algorithm and chaotic Baker map interleaving. The compressed video frames have self-embedded digital signatures, and the transmission is performed over Orthogonal Frequency Division Multiplexing wireless systems with equalization. The video frames compressed with SPIHT algorithm are primarily converted to the luminance and chrominance scheme for self-watermark embedding, and then the binary format is reconstructed again. Prior to the modulation step, the chaotic randomization is performed on the binary data to encrypt the data to be transmitted and reduce the noise impact of the multipath channel. For performance evaluation, both Rayleigh and additive noise channels are considered for various video frames with chaotic interleaving. The simulation results reveal that the received watermarked video frames with chaotic interleaving applied achieve higher Peak Signal-to-Noise Ratios and correlation coefficients compared to other approaches.

Robust Image Embedded Watermarking Using DCT and Listless SPIHT

This paper presents a DCT-based (DCT: discrete cosine transform) listless set partitioning in hierarchical trees (SPIHT) digital watermarking technique that is robust against several common attacks such as cropping, filtering, sharpening, noise, inversion, contrast manipulation, and compression. The proposed technique is made further robust by the incorporation of the Chinese remainder theorem (CRT) encryption technique. Our scheme is compared with the recently proposed CRT-based DCT technique, CRT-based spatial domain watermarking, and DCT-based inter block correlation techniques. Extensive simulation experiments show better robustness in common image manipulations and, at the same time, the proposed technique successfully makes the watermark perceptually invisible. A better Tamper Assessment Function (TAF) value of 2–15% and a better Normalized Correlation (NC) is achieved compared to some of the above techniques. In particular, the proposed technique shows better robustness on compression attacks at moderate to higher compression ratios. It is possible to maintain the imperceptibility and low TAF for various values by doubling the capacity of the watermark.

Computational Paradigm and Quantitative Optimization to Parallel Processing Performance of Still Image Compression

Fashionable and staggering evolution in inferring the parallel processing routine coupled with the necessity to amass and distribute huge magnitude of digital records especially still images has fetched an amount of confronts for researchers and other stakeholders. These disputes exorbitantly outlay and maneuvers the digital information among others, subsists the spotlight of the research civilization in topical days and encompasses the lead to the exploration of image compression methods that can accomplish exceptional outcomes. One of those practices is the parallel processing of a diversity of compression techniques, which facilitates split, an image into ingredients of reverse occurrences and has the benefit of great compression. This manuscript scrutinizes the computational intricacy and the quantitative optimization of diverse still image compression tactics and additional accede to the recital of parallel processing. The computational efficacy is analyzed and estimated with respect to the Central Processing Unit (CPU) as well as Graphical Processing Unit (GPU). The PSNR (Peak Signal to Noise Ratio) is exercised to guesstimate image re-enactment and eminence in harmonization. The moments are obtained and conferred with support on different still image compression algorithms such as Block Truncation Coding (BTC), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DTCWT), Set Partitioning in Hierarchical Trees (SPIHT), Embedded Zero-tree Wavelet (EZW). The evaluation is conceded in provisos of coding efficacy, memory constraints, image quantity and quality.

An Efficient Source–Channel Coding for Wireless Image Transmission Over Underwater Acoustic Channel

In this paper, a complete system for image transmission in harsh underwater environment is proposed. The key to increase the performance of the system is the use of an efficient image compression algorithm with a bandwidth-efficient modulation technique. The wavelet packet (WP) decomposition is used to get the best image representation and the set partitioning in hierarchical trees is applied on the WP coefficients. The parental conflicts are resolved, the parent–child relationships are adapted and thus the similarities between cross-subbands are preserved. Reed–Solomon is used for forward error correction to combat with the errors in wireless transmission. Orthogonal frequency division multiplexing with differential quadrature phase shift keying is used to transmit the generated bit stream. Effective image quality metrics are used for objective evaluation. Results show that the proposed system manages to transmit images over the limited bandwidth, and to effectively minimize the perceptual degradation.

Comparative Performance Evaluation of Three Image Compression Algorithms

The advent of computer and internet has brought about massive change to the ways images are being managed. This revolution has resulted in changes in image processing and management as well as the huge space requirement for images’ uploading, downloading, transferring and storing nowadays. In guiding against this huge space requirement, images need to be compressed before either storing or transmitting. Several algorithms or techniques on image compression had been developed in literature. In this study, three of these image compression algorithms were developed using MATLAB codes. The three algorithms developed are discrete cosine transform (DCT), discrete wavelet transform (DWT) and set partitioning in hierarchical tree (SPIHT). In order to ascertain which of them is most appropriate for image storing and transmission, comparative performance evaluations were conducted on the three developed algorithms using five performance indices. The results of the comparative performance evaluations show that the three algorithms are effective in image compression but with different efficiency rates. In addition, the comparative performance evaluations results show that DWT has the highest compression ratio and distortion level while the corresponding values for SPIHT is the lowest with those of DCT fall in-between. Also, the results of the study show that the lower the mean square error and the higher the peak signal-to-noise-ratio, the lower the distortion level in the compressed image.

Embedded Image Compression: A Review

In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.

Three‐dimensional separate descendant‐based SPIHT algorithm for fast compression of high‐resolution medical image sequences

To provide a fast compression algorithm for high-resolution medical image sequences, an efficient three-dimensional (3D) separate descendant-based (SBD) set partitioning in hierarchical trees (SPIHT) algorithm (3D SDB-SPIHT) is proposed in this study. To accelerate the transformation, 3D integer wavelet transform is used first. Based on an efficient spatial–temporal tree structure, which is designed for the transformed coefficients, the authors propose a fast coding scheme by separating the descendant set into offspring set and leaves set. The proposed algorithm has more selectivity in deciding the scanning and coding of the descendant sets and hence the coding time is accelerated. Experimental results demonstrate that 3D SDB-SPIHT compresses medical images faster compared with traditional 3D SPIHT and other variations of 3D SPIHT.

Medical Image Compression Using Roi and Nroi Images

Advanced medical imaging requires storage space of huge quantities of digitized clinical data. Due to the inhibited bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in spatial domain will be changed into spectral domain after the transformation to attain more compression gains. In our proposed system, separation of the original image in to Region of Interest (ROI) and Non-Region of Interest (NROI) parts is done. Then Lossless compression with Set Partition in Hierarchical Trees (SPIHT) algorithm is used to compress the ROI part and Lossy compression with Haar wavelet transform is used to compress the NON-ROI part. This algorithm gives better Peak Signal Noise Ratio (PSNR)and Bits per pixel (BPP) value for medical images.

Method for pre-processing of level crossing image

Summary. Actuality of problem in the improvement of transport safety at level crossings (LC) is caused by increasing the number of vehicles and reducing discipline of vehicle drivers. One of ways for solution of this problem is associated with using the video surveillance systems for monitoring danger area of level crossing. In such systems due to the limited bandwidth of data channel usually the image compression techniques are used. In this paper the pre-processing method for compression of images is presented. Proposed method accounts unequal subjective informational content of different LC image regions (using fuzzy logic and wavelet transform). Comparison of this method with plain set partitioning in hierarchical trees (SPIHT) technique showed that proposed method allows obtaining better result at image compression in terms of reconstruction quality and compression ratio.