SPIHT Research Articles

Computational efficient method for ECG signal compression based on modified SPIHT technique

In this paper, an improved method for electrocardiogram (ECG) signal compression using Set Partitioning in Hierarchical Trees (SPIHT) algorithm is proposed. ECG signals are compressed based on different transform such as discrete cosine transform and discrete wavelet transform with modified SPIHT. The modified SPIHT algorithm yields good compression with controlled quantity of signal degradation and requires computational time as compared to earlier published SPIHT algorithms. The proposed algorithm is suitable for the ECG signal compression for telemedicine or e-health system due to minimum computational time.

Dynamic Dictionary for Combined EEG Compression and Seizure Detection

A novel technique for real-time electroencephalogram (EEG) compression is proposed in this paper. This technique makes use of the redundancy between the different frequency subbands present in EEG segments of one channel. It uses discrete wavelet transform (DWT) and dynamic reference lists to compute and send the decorrelated subband coefficients. Set partitioning in hierarchical trees (SPIHT) is also used as source coder. Experimental results showed that the proposed method can not only compress EEG channels in one dimension (1- D), but also detect seizure-like activity. A diagnostics-oriented performance assessment was performed to evaluate the performance of both the compression and detection capabilities of the proposed method. In this paper, we show that the algorithm can positively detect seizure sections in the recordings at bitrates down to 2 bits per sample.

Representation of Image Data by New Lifting based Biorthogonal Wavelets and Its Application to Lossless Compression

In this paper an attempt has been made to derive the lifting scheme for a set of new bi-orthogonal wavelets and apply on image compression. Four new bi-orthogonal wavelets are designed by taking different basis functions which are selected so as to capture the sharp edges which are common in images. For these classical wavelets, lifting versions are calculated and presented in this paper. The lifting step calculation is simplest among the schemes in related literature. From the designed wavelet filters, the poly-phase matrix is written and decomposed the matrix into a form from which lifting steps are directly available. To incorporate coding Set Partitioning In Hierarchical Trees (SPIHT) algorithm is used. The Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), Transforming Time (TT), Encoding Time (ET) and Decoding Times (DT) are calculated. It was found that the compression performance of new classical wavelets is a little better than that of existing classical/first generation wavelets and that of new lifting based wavelets is far better than that of existing classical wavelets and also existing lifting based wavelets.

Wavelet Based Image Compression, Pattern Recognition And Data Hiding

Images require substantial storage and transmission resources, thus image compression is advantageous to overcome these difficulties. This paper presents Application of DWT for image processing. The main objectives of this paper are image compression, reconstruction, pattern recognition and image data hiding. First, in compression of two-dimensional image data is divided an image into blocks of size N x M and then computed the DWT of each block at the finest spatial resolution. The blocks of transformed coefficients were classified into type HH, HL, LH and LL. Again, block LL can be used to classify four more blocks in second level. Each class consisted of group of blocks of size (N/2) x (M/2) of similar type. Then these blocks are quantized using SPIHT (Set Partitioning in Hierarchical Trees) Algorithm to get a compressed image (i.e. bit stream). Reconstruction of image follows exactly reverse process i.e. inverse SPIHT and inverse DWT. The reconstructed image will be equivalent to original image. New algorithms are developed for pattern recognition and image data hiding, Secondly DWT based feature for pattern recognition. This algorithm is based on binary comparison using X-OR operation and Hamming distance algorithms. Since here binary comparison is done, the DWT is used to enable better recognition accuracy. Finally, this paper presents image data hiding. In this, compressed image (bit stream image or called signature image) is embedded into a dummy image (or called host image). This is done using bit replacement technique. The quality of embedded image cannot be identified by human visual system.

无人机侦察图像压缩

To reduce the storage space of image data and to recover the high quality image of an interested region,a method of segmented compression reconfiguration based on the interested region was proposed.Firstly,the Region of Interest(ROI)was extracted from the whole image according to some regularity of surveillance targets.The original image was then divided into ROI and Region of Background(BG)by using the segmentation algorithm.Finally,with the compression method based on wavelet transform,the ROI and BG region were compressed with different compression ratios by using the embedded coding of Set Partitioning in Hierarchical Trees(SPIHT).Simulation experiment shows that the method gives higher compression efficiency and the recovered image is more suitable for human eye under the same condition.Compared to other methods,this method increases the Peak Signal to Noise Ratio(PSNR)of image and solves the contradiction of the high compression ratio to high quality.

Optimum Bit Rate for Image Transmission over Underwater Acoustic Channel

In this paper, image transmission in underwater channels is considered. The images are encoded with forward error correction using an unequal error protection technique together with the Reed-Solomon codes and dynamic bit-rate allocation before transmitted. This paper proposes a novel rate allocation scheme for efficient image bit stream transmission in underwater acoustic channels with optimum bit rates. The optimality is achieved in the sense that the comprehensive peak signal–to–noise ratio of the image transmission is maximized under channel bit rate and bit error rate constraints. Based on a modified set partitioning in hierarchical trees (M-SPIHT) image coder, four different flocks of bit-streams based on their significance levels are generated. According to their significance levels, the blocks of the significant bits, the sign bits, the set bits and the refinement bits are transmitted with different protection levels, so as to reduce the total distortion of received image. In addition to the careful selection of each component and intuitive justification in the detailed system design, simulation results have also been included. It is demonstrated that the proposed scheme outperforms the equal error protection for image transmission in underwater channels, significantly improves the peak signals–to–noise ratio (PSNR) performance in comparison to existing coding schemes.

Highly Scalable Compression Method for Super Resolution Multi Spectral Images

This paper provides a new technique for lossy multispectral images compression at very high data rates. Image compression is becoming more and more important, as new multispectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. The Lossless compression does not provide a sufficient degree of data volume reduction to meet the bandwidth requirements of the downlink channel, so a lossy approach is the only possible solution. Multispectral data exhibits a large spectral correlation. Many techniques have been proposed for multispectral image data. Mainly in all the compression techniques to minimize the intra-band and inter-band correlation, some of them do in a single step and others do in a two-step transformation followed by an encoding scheme to eliminate the statistical redundancy or to give a desired bit-rate at low compression ratios based on the wavelet transform, where the spatial and the spectral redundancy are removed simultaneously. At higher compression ratios, a high quality wavelet based method in the spatial dimensions combined with the Improved Set Partitioning Hierarchical Trees (ISPHIT) in the spectral dimension gives the best results.

A Unified Scheme For Fast And Efficient Spiht Coding Algorithm Used For Image Compression

This paper concerns Image compression obtained with wavelet-based compression techniques such as setpartitioning in hierarchical trees (SPIHT)yield very good results The necessity in image compression continuously grows during the last decade, different types of methods is used for this mainly EZW, SPIHT and others. In this paper we used discrete wavelet transform and after this set-partitioning in hierarchical trees (SPIHT) with some improvement in respect of encoding and decoding time with better PSNR with respect to EZW coding.

Region-of-Interest Coding based on Fovea and Hierarchical Trees

Image and video compression exploits the redundancy of data to create a smaller representation. Lossy compression can be considered to be a type of transform coding where the raw data is transformed to a domain. Such a transform coding stores most of image energy into very few coefficients. In this paper we propose a compression algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) that exploits the Human Visual System (HVS) and its fovea. In order to increase the image quality of the reconstructed image, regions of interest (ROI) are defined around a given point of gaze. The use of a fovea combined with ROI for image compression can help to improve the quality of the perception of the image and preserve different levels of detail around the ROI. In the proposed approach, the image is compressed using the Lifting Wavelet Transform and then quantized at multiple compression ratios around the point of fixation of an observer, taking advantage of the natural aliasing of the HVS around the fovea. Such a compression delivers better image or frame reconstruction when a fixation point of an observer is given. DOI: http://dx.doi.org/10.5755/j01.itc.42.4.3076

A multiple description coding based on SPIHT algorithm with WiMAX modulations for color images

This study presents a multiple description coding (MDC) with WiMAX modulations for color images. The proposed approach uses the set partitioning in hierarchical trees (SPIHT) algorithm to compress images, employs a poly-phase sampling technique to generate multiple descriptions and develops a transmitting principle for WiMAX to adapt to varying wireless network environments. As a result, our proposed method achieves high performance of image compression, obtains superior capabilities of conquering packet losses and has excellent quality of reconstructed images. Besides, by encoding the three components (Red, Green, and Blue) separately and interleaving the produced encoding results for transmission it extends progressive reconstruction for a color image. Furthermore, the proposed method is highly flexible for practical applications. Extensive experiments are performed and their results demonstrate promising characteristics and performances of the proposed method.

English

Generally lossless compression should be used for ROI (Region of Interest) and lossy compression should be used for ROB (Region of Background) with a lower quality. In existing system, Region of Interest (ROI) is selected manually, but ROI is selected automatically in the proposed method, pre-processing is done to improve the visual quality of the image. Segmentation is carried out accurately and efficiently using region growing followed by morphological processing method. The features are extracted and classification is done in medical image using Fuzzy logic. ROB part of an image is compressed using SPIHT (Set Partition In Hierarchical Tree) algorithm in near lossless manner. Finally the ROI is superimposed in compressed Non ROI (ROB) image. This method improves the compression ratio and increase the PSNR value compared to existing method. The proposed method is used for implementations of teleradiology and digital picture archiving and communications (PACS) systems practically. Key words: Image compression, segmentation, wavelet decomposition, fuzzy logic , decompression, compression ratio.

Optical image compression based on adaptive directional prediction discrete wavelet transform

The traditional lifting wavelet transform cannot effectively reconstruct the nonhorizontal and nonvertical high-frequency information of an image. In this paper, we present a new image compression method based on adaptive directional prediction discrete wavelet transform (ADP-DWT). We first design a directional prediction model to obtain the optimal transform direction of the lifting wavelet. Then, we execute the directional lifting transform along the optimal transform direction. The edge and texture energy can be reduced in the nonhorizontal and nonvertical directions of the high-frequency sub-bands. Finally, the wavelet coefficients are coded with the set partitioning in hierarchical trees (SPIHT) algorithm. The new method holds the advantages of both adaptive directional lifting (ADL) and direction-adaptive discrete wavelet transform (DA-DWT), and the computational complexity is far lower than that in these methods. For the images containing regular and fine textures or edges, the coding preformance of ADP-DWT is better than that of ADL and DA-DWT.

ANALYSIS OF IMAGE COMPRESSION ALGORITHMS USING WAVLET TRANSFORM WITH GUI IN MATLAB

Image compression is nothing but reducing the amount of data required to represent an image. To compress an image efficiently we use various techniques to decrease the space and to increase the efficiency of transfer of the images over network for better access. This paper explains about compression methods such as JPEG 2000, EZW, SPIHT (Set Partition in Hierarchical Trees) and HSSPIHT on the basis of processing time, error comparison, mean square error, peak signal to noise ratio and compression ratio. Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in many conditions especially in the memory constraint case. SPIHT gives better simplicity and better compression compared to the other techniques. But to scale the image more so as to get better compression we are using the line-based Wavelet transform because it requires lower memory without affecting the result of Wavelet transform. We proposed a highly scalable image compression scheme based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. This algorithm is called Highly Scalable SPIHT (HS_SPIHT) it gives good scalability and provides 1 bit stream that can be easily adapted to give bandwidth and resolution requirements.

A Novel VLSI Architecture for SPHIT Encoder

In this Paper we propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. Our algorithm called highly scalable SPIHT (HS-SPIHT), supports spatial and SNR scalability and provides a bit stream that can be easily adapted (reordered) to given bandwidth and resolution requirements by a simple transcoder (parser). The HS-SPIHT algorithm adds the spatial scalability feature without sacrificing the SNR embeddedness property as found in the original SPIHT bit stream. HS-SPIHT finds applications in progressive Web browsing, flexible image storage and retrieval, and image transmission over heterogeneous networks. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.

Exploiting chrominance planes similarity on listless quadtree coders

This study proposes an efficient algorithm for colour image compression with listless implementation based on set partition block embedded coding (SPECK). The objective of this work is to develop an algorithm that exploits the redundancy in colour spaces, low complexity quadtree partitioning and reduced memory requirements. Colour images are first transformed into luminance chrominance (YCbCr) planes and a wavelet transform is applied. A reduction of the memory requirement is achieved with the introduction of a state marker that matches each colour plane to eliminate the list with dynamic memory in the original colour SPECK coder (CSPECK). The wavelet coefficients are scanned using Z-order that matches the subband decompositions. The proposed algorithm then encodes the de-correlated colour plane as one unit and generates a mixed bit stream. The linear indexing and initial state marker are modified to jointly test the chrominance plane together. Composite colour coding enables precise control of the bit rate. The performance of the proposed algorithm is comparable with CSPECK, set partitioning in hierarchical trees (SPIHT) and JPEG2000 but with less memory requirements. For progressive lossless, a saving of more than 70\% than final working memory against CSPECK and SPIHT highlights the benefit of the proposed algorithm.

A Secure Fast 2D-Discrete Fractional Fourier Transform Based Medical Image Compression Using SPIHT Algorithm with Huffman Encoder

The arrival of CT and MRI imaging in the last two decades used for analysis of various diseases in medical field. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) sequences have become essential in healthcare systems and an integral segment of a patient’s medical record. Such medical image data needed a huge amount of resources for storage and transmission. Compression of medical images for efficient use of storage space and transmission bit rate has become a necessity. A quasigroup successfully creates an astronomical number of keys which is used to confuse the hackers in determining the original data. However, quasi group encryption is not capable in diffusing the information of the plain text. Hence, in this paper the DICOM images are encrypted using a chained Hadamard transforms and Number Theoretic Transforms to introduce diffusion along with the quasigroup transformation. A secure Fast Two Dimensional Discrete Fractional Fourier Transform (DFRFT) and a SPIHT Algorithm with Huffman Encoder is used for compress the image and it provides a secure compression of medical images as compared to the other transform. The experimental results evaluate the performance of the proposed encryption approach based on the PSNR and MSE it shows the proposed approach gives better results.

A novel medical image compression using Ripplet transform

In spite of great advancements in multimedia data storage and communication technologies, compression of medical data remains challenging. This paper presents a novel compression method for the compression of medical images. The proposed method uses Ripplet transform to represent singularities along arbitrarily shaped curves and Set Partitioning in Hierarchical Trees encoder to encode the significant coefficients. The main objective of the proposed method is to provide high quality compressed images by representing images at different scales and directions and to achieve high compression ratio. Experimental results obtained on a set of medical images demonstrate that besides providing multiresolution and high directionality, the proposed method attains high Peak Signal to Noise Ratio and significant compression ratio as compared with conventional and state-of-art compression methods.

A Study of image compression based transmission algorithm Using SPIHT for low bit rate application

Image compression is internationally recognized up to the minute tools for decrease the communication bandwidth and save the transmitting power. It should reproduce a good quality image after compression at low bit rates. Set partitioning in hierarchical trees (SPIHT) is wavelet based computationally very fast and among the best image compression based transmission algorithm that offers good compression ratios, fast execution time and good image quality. Precise Rate Control (PRC) is the distinct characteristic of SPIHT. Image compression-based on Precise Rate Control and fast coding time are principally analyzed in this paper. Experimental result shows that, in the case of low bit-rate, the modified algorithm with fast Coding Time and Precise Rate Control can reduce the execution time and improves the quality of reconstructed image in both PSNR and perceptual when compare to at the same low bit rate.

Listless Block Tree Coding with Discrete Wavelet Transform for Embedded Image Compression at Low Bit Rate

Listless Block Tree Coding (LBTC) is Wavelet Block Truncation Coding (WBTC) algorithm without using lists. LBTC is evolved from two variants of Set Partitioning In Hierarchal Trees (SPIHT) which are Wavelet based Block Tree Coding (WBTC) and No list SPIHT (NLS). WBTC works on blocks with varying root sizes instead of pixels so it lowers the memory requirement as compared to SPIHT and uses three ordered auxiliary lists to keep track of the significant coefficients similar to SPIHT. SPIHT uses lists which keep on increasing at each pass and require o lot of memory, so another variant of SPIHT is NLS which uses a state table to store the significant coefficients. This state table uses four bits for each coefficient. In LBTC, listless variant of WBTC is implemented using the concept of NLS in which instead of lists, markers are used. This paper presents the proposed algorithm in which LBTC is combined with DWT and the quality of compressed image is improved significantly by optimizing the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). It outperforms other compression methods by a wide margin in terms of PSNR and MSE though the time taken by the algorithm is more than LBTC.

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 × 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.