Threshold setting model for the set partitioning in hierarchical trees (SPIHT) for audio

In this paper, a new multiple description coding (MDC) method for color images is presented. The proposed approach bases on the set partitioning in hierarchical trees (SPIHT) algorithm to achieve a high image compression ratio; employs poly-phase sampling technique to generate multiple descriptions to adapt to varying internet environments. It results in having capabilities to conquer packet losses and remaining the reconstructed images with an acceptable quality. In order to preserve the progressive reconstruction properties for a color image transmission, the three components (Red, Green, and Blue) are encoded separately and their encoding results are interleaved for transmission. The proposed method is applicable to color images and is very flexible for practical applications. The experimental results are given to illustrate the characteristics and verify the efficiency of the proposed method.

We have considered, in this paper, two compression methods based on wavelet transform and progressive coding, called EZW (Embedded Zerotree Wavelet) and SPIHT (Set partitioning in hierarchical trees). These techniques provide the opportunity to significantly increase compression ratios while preserving high qualities of reconstructed medical images. Unlike the EZW, the SPIHT approach takes advantage of subband correlations at near and far resolution levels (father-son relationships). A partial ordering by amplitude of the wavelet coefficients of the DWT (Discrete Wavelet Transform), partitioning in hierarchical trees, and scheduling of the transmission of the refinement bits are the three ideas used by SPIHT (the amplitude of each significant coefficient is progressively refined). The goal is to create a digital tool with the required main compression rate and PSNR (Peak Signal to Noise Ratio) limitations for compressing medical images. A development of the two cited algorithms has been carried out. For their evaluation, several medical images from two databases were used. The results prove that these algorithms are very efficient. Also, it has been found that the obtained results with SPIHT method greatly exceed those provided by EZW approach. SPIHT approach is very efficient compared to EZW from the point of view of the compression ratio and the image quality.

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.

This paper describes a family of list-free tree-set scanning (LIFTS) algorithms, related to Shapiro's (1993) embedded zero-tree wavelet (EZW) coding and to Said and Pearlman's (see IEEE Trans. on Circuits and Systems for Video Technology, vol.6, no.3, p.243-9, 1996) set partitioning in hierarchical trees (SPIHT) algorithm. The principal advantage of the LIFTS algorithms is that they are parallelizable and require greatly reduced computational complexity and memory overhead, while nearly achieving the efficiency of SPIHT. The algorithms may be tailored to special requirements, such as transmission of a fully embedded bit-stream with sub-image refinement.

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with higher performance, clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec and the DCT based AGP image codec (DCT- AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7 dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform. The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT- AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low- complexity and high performance codec may provide a new direction for the implementation of image compression.

Wavelet-based image compression is proving to be a very effective technique for medical images, giving significantly better results than the JPEG algorithm. A novel scheme for encoding wavelet coefficients, termed set partitioning in hierarchical trees, has recently been proposed and yields significantly better compression than more standard methods. The authors report the results of experiments comparing such coding to more conventional wavelet compression and to JPEG compression on several types of medical images.

To prolong the life cycle of wireless sensor network, the basic theory of wavelet transform and its application in image compression are described, and several classic image compression algorithms based on wavelet transform are studied in depth. A compression algorithm combining the improved wavelet transform and Set Partitioning in Hierarchical Trees (SPIHT) algorithm of hierarchical wavelet tree set segmentation is proposed to effectively balance the energy consumption of each node in the sensor network and prolong the life of the whole wireless sensor network and an improved distributed image compression and transmission algorithm is proposed based on the distributed multi-node cooperative processing algorithm based on wavelet transformation, and detailed analytical test and energy consumption simulation experiment are carried out to verify the feasibility of the algorithm. The results show that the platform effectively implements and verifies the proposed algorithm, which can effectively realize the compression and transmission of distributed images, equalize the energy consumption of each sensor node in the network, and has strong practicability.

The recent innovation in digital medical imaging techniques requires the development of high-performance storage and image transmission systems. The biggest problem in digital technology is voluminous amount of data generated. For example, a digital mammographic image of size 1024 × 1024 pixels with 8 bits per pixel requires 8.3 Mb for storing it in original form. Researchers in several studies have demonstrated the need for high rate compression algorithms for medical imaging applications. But the recently reported image compression results indicate that Peak Signal-to-Noise Ratio (PSNR) is outperformed by scalar wavelets. However, it often fails to capture high-frequency information accurately. Interestingly, multiwavelet preserves high-frequency information in mammographic image and provides good energy compaction. The challenge still remains as to how one can better represent the signal for achieving the best compression. This paper proposes a solution to the above open problems using balanced multiwavelet-based image compression. The proposed scheme presents combination of two novel ideas: A coefficient reorganization suitable to balanced multiwavelet decomposition is used to regain the parent-child relationship. A block tree coding is used for compression and reconstruction of multiwavelet transformed image and thus the proposed scheme is called as Multiwavelet Block Tree Coding (MBTC). This approach yields the advantages of high energy compaction, PSNR, and a less number of bits for encoding. Balanced multiwavelet-based compression, with MBTC applied to a set of four mammographic images, achieves an average PSNR of 43.245 dB against the existing Set Partitioning In Hierarchical Trees (SPIHT) algorithm which on an average achieves only 34.181 dB for 0.5 bpp bit rate and MBTC requires encoding bits of 45.565% less than SPIHT encoding.

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.

Generally synthetic aperture radar (SAR) image compression methods based on wavelet transform remove statistic redundancy of image data and neglect visual redundancy. In view of this problem, a new image compression method based on human visual system (HVS) is proposed in this paper. First SAR image is decomposed by wavelet transform, then wavelet coefficients in different subbands are weighted by the peak of contrast sensitivity function (CSF) curve in wavelet domain, at last set partitioning in hierarchical trees (SPIHT) algorithm is used to code the weighted wavelet coefficients to form embedded bit stream. Compression results show that comparing with conventional SPIHT algorithm, the method proposed in this paper gets better subject visual quality at the same compression ratio with almost equivalent objective evaluation results

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.

This chapter is devoted to the exposition of a complete video coding system, which is based on coding of three dimensional (wavelet) subbands with the SPIHT (set partitioning in hierarchical trees) coding algorithm. The SPIHT algorithm, which has proved so successful in still image coding, is also shown to be quite effective in video coding, while retaining its attributes of complete embeddedness and scalability by fidelity and resolution. Three-dimensional spatio-temporal orientation trees coupled with powerful SPIHT sorting and refinement renders 3D SPIHT video coder so efficient that it provides performance superior to that of MPEG-2 and comparable to that of H.263 with minimal system complexity. Extension to color-embedded video coding is accomplished without explicit bit-allocation, and can be used for any color plane representation. In addition to being rate scalable, the proposed video coder allows multiresolution scalability in encoding and decoding in both time and space from one bit-stream. These attributes of scalability, lacking in MPEG-2 and H.263, along with many desirable features, such as full embedded-ness for progressive transmission, precise rate control for constant bit-rate (CBR) traffic, and low-complexity for possible software-only video applications, makes the proposed video coder an attractive candidate for for multi-media applications. Moreover, the codec is fast and efficient from low to high rates, obviating the need for a different standard for each rate range.

We extend the work of Sherwood and Zeger to progressive video coding for noisy channels. By utilizing a 3D extension of the set partitioning in hierarchical trees (SPIHT) algorithm, we cascade the resulting 3D SPIHT video coder with a rate-compatible punctured convolutional channel coder for transmission of video over a binary symmetric channel. Progressive coding is achieved by increasing the target rate of the 3D embedded SPIHT video coder as the channel condition improves. The performance of our proposed coding system is acceptable at low transmission rate and bad channel conditions. Its low complexity makes it suitable for emerging applications such as video over wireless channels.

Medical images must be compressed before transmission due to bandwidth and storage limitations. The set partitioning in hierar- chical trees (SPIHT) algorithm is an efficient method for lossy and loss- less coding of medical images. We propose some modifications to the SPIHT algorithm. It is based on the idea of the insignificant correlation of wavelet coefficients among medium- and high-frequency subbands. In this scheme, insignificant wavelet coefficients that correspond to the same spatial location in the medium subbands can be used to reduce the redundancy by a combined function proposed in associated with the modified SPIHT. In high-frequency subbands, the modified SPIHT pro- poses a dictator to reduce the interband redundancy. Experimental re- sults indicate that the proposed technique improves the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. © 2003 Society of Photo-Optical Instrumentation Engi- neers. (DOI: 10.1117/1.1578645)

Recently many new algorithms for image compression based on wavelets have been developed.This paper gives a detailed explanation of SPIHT algorithm with the combination of Lempel Ziv Welch compression technique for image compression by MATLAB implementation. Set partitioning in Hierarchical trees (SPIHT) is one of the most efficient algorithm known today. Pyramid structures have been created by the SPIHT algorithm based on a wavelet decomposition of an image. Lempel Ziv Welch is a universal lossless data compression algorithm guarantees that the original information can be exactly reproduced from the compressed data.The proposed methods have better compression ratio, computational speed and good reconstruction quality of the image. To analysis the proposed lossless methods here, calculate the performance metrics as Compression ratio, Mean square error, Peak signal to Noise ratio. Key Words-LempelZivWelch (LZW),SPIHT,Wavelet