Generalization of SPIHT: Set Partition Coding System

Li, Qiufu; Chen, Derong; Jiang, Wei; Liu, Bingtai; Gong, Jiulu

doi:10.1109/tip.2015.2509253

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…Similarly, in [48], the author proposed a novel generalized SPIHT algorithm, called set partitioning coding system (SPACS), which has good performance. Therefore, in Table 3, we make a brief comparison between our method and that of SPACS.…”

Section: Resultsmentioning

confidence: 99%

A combined HMM–PCNN model in the contourlet domain for image data compression

Yang

et al. 2020

PLoS ONE

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Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data such as edges and contours. Therefore, researchers have been exploring new MGA-based image compression standards rather than the JPEG2000 standard. However, due to the difference in terms of the data structure, redundancy and decorrelation between wavelet and MGA, as well as the complexity of the coding scheme, so far, no definitive researches have been reported on the MGA-based image coding schemes. In addressing this problem, this paper proposes an image data compression approach using the hidden Markov model (HMM)/pulse-coupled neural network (PCNN) model in the contourlet domain. First, a sparse decomposition of an image was performed using a contourlet transform to obtain the coefficients that show the multiscale and multidirectional characteristics. An HMM was then adopted to establish links between coefficients in neighboring subbands of different levels and directions. An Expectation-Maximization (EM) algorithm was also adopted in training the HMM in order to estimate the state probability matrix, which maintains the same structure of the contourlet decomposition coefficients. In addition, each state probability can be classified by the PCNN based on the state probability distribution. Experimental results show that the HMM/PCNN-contourlet model proposed in this paper leads to better compression performance and offer a more flexible encoding scheme.

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Section: Resultsmentioning

confidence: 99%

A combined HMM–PCNN model in the contourlet domain for image data compression

Yang

et al. 2020

PLoS ONE

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“…Although the EZW is a very standard technique for wavelet encoding, but there are certain limitations with it as; this technique deals with splitting the LL sub bands only & do not consider other sub bands splitting, another limitation is that, it is exploiting redundancy which is present at a particular spatial position but across different scale, but it is not exploiting the redundancy that exists among neighborhood coe cients of the same sub band. This issue associated with EZW is solved using Set partitioning in Hierarchical tree (SPIHT) algorithm [19].…”

Section: X| < T = Insignficant |X| ⩾ T = Significantmentioning

confidence: 99%

Development of Wavelet Based Image Compression Approach Using Modified SPIHT Algorithm

Shelke

Sinha

Patel³

2022

Preprint

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In case of images & videos, the encoding philosophy is substantially different as compared to that of speech. The very basic difference between image & speech is that, the speech signals are one dimensional, whereas the images are two dimensional in nature. Modern day images can have the resolution in the range of ‘1024 x 1024’, this means there will 1 Mega Pixels & for color images, 1 pixel is represented by 24 bits (8 bits for each pixel component of red, green & blue), that means there are 24 Megabits in a single still image. In videos such still images are changing at 30 frames per second, so the basic bit rate going to be 30 times of 24 Megabits, this leads to the explosion in information. This demands compression of images at significant extent. In this work, image compression is achieved by using transformation based on modified lifting wavelet structure followed by quantization based on modified set partition in hierarchical tree (SPIHT) algorithm. This modification in SPIHT algorithm helps to reduce the bit stream length of compressed signal to significant extent as compared to conventional SPIHT algorithm. The proposed architecture is developed with Verilog language and simulated with modelsim simulator and synthesized with XST(VHDL/Verilog) tool of Xilinx software under Virtex6-XC6VLX240TD environment. The comparative analysis of proposed system with existing system shows that, there is significant improvement in compression ratio & PSNR.

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“…We use the set partitioning in hierarchical trees (SPIHT) algorithm as the image compression algorithm. The SPIHT algorithm has extremely low computational complexity and high-quality recovery characteristic and breaks the boundary between coding efficiency and complexity in traditional coding algorithms [51]. It makes reasonable use of multi-resolution characteristics after wavelet decomposition and has good coding performance.…”

Section: Design and Performance Simulation Of The Adaptive Rc-nb-lmentioning

confidence: 99%

“…The objective evaluation method usually uses the error between the reconstructed image and the original image to measure the quality of the reconstructed image. Common evaluation methods include Mean Squared Error (MSE) and Peak Signal Noise Ratio (PSNR) [51]. The calculation formula for MSE is:MSE=true∑i=1Mtrue∑j=1N(f(i,j)−f(i,j)′)2M×N where f(i,j) and f(i,j)′ represent the pixel values of the original image and the reconstructed image at (i,j), respectively, and M×N is the size of the image.…”

Section: Design and Performance Simulation Of The Adaptive Rc-nb-lmentioning

confidence: 99%

Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System

Zhao

Tian

Xue

2019

Sensors

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This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality.

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Generalization of SPIHT: Set Partition Coding System

Cited by 11 publications

References 15 publications

A combined HMM–PCNN model in the contourlet domain for image data compression

A combined HMM–PCNN model in the contourlet domain for image data compression

Development of Wavelet Based Image Compression Approach Using Modified SPIHT Algorithm

Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System

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