Set Partitioning in Hierarchical Frequency Bands (SPHFB)

Ochoa, Humberto; Villegas, Osslan Osiris Vergara; Sánchez, Vianey Guadalupe Cruz; Rosiles, Gerardo; Vega-Pineda, Javier

doi:10.1109/dcc.2009.63

Cited by 2 publications

(4 citation statements)

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“…However, only for low bit rates, the PSNR is the same as SPIHT. The work that considers the hierarchical scanning of subbands is the SPHFB, 17 which makes use of Eq. (5), resulting in about the same number of comparison operations as the modified SPIHT.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, there is a great interest to improve its performance. 17,21,22 Besides, it provides progressive image transmission, a fully embedded coded file, a simple quantization algorithm, lossless compression, and exact bit rate coding. Furthermore, SPIHT is very useful for applications where the user can quickly inspect the image and decide if it should be downloaded, is good enough to be saved, or needs refinement.…”

Section: Introductionmentioning

confidence: 99%

“…Set partitioning in hierarchical frequency bands (SPHFB) 17 uses the set partitioning technique to sort the transform coefficients. The threshold of each subband is calculated, and the subbands scanning sequence is determined by the magnitude of the thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12] SPIHT-based image compression can achieve rate scalability and high coding performance. [13][14][15][16][17][18][19][20] The encoder makes use of progressive transmission to exploit the selfsimilarity of the DWT coefficients across different scales. In addition, to iteratively extract the most important information in each subband, SPIHT uses partial ordering by comparing the transformed coefficient's magnitudes with a set of octave decreasing thresholds.…”

Section: Introductionmentioning

confidence: 99%

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Modified set partitioning in hierarchical trees algorithm based on hierarchical subbands

Domínguez

Villegas

Sánchez

2015

J. Electron. Imaging

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Abstract. This paper introduces a modified set partitioning in hierarchical trees (SPIHT) algorithm that reduces the number of comparison operations and, consequently, the execution time needed to encode an image as compared to the SPIHT algorithm. The threshold of each independent subband is calculated after applying the discrete wavelet transform to the image. Scanning of the sets inside the subbands is determined by the magnitude of the thresholds that establishes a hierarchical scanning not only for the set of coefficients with larger magnitude, but also for the subbands. The algorithm uses the set partitioning technique to sort the transform coefficients. Results show that the modified SPIHT significantly reduces the number of operations and the execution time without sacrificing visual quality and the PSNR of the recovered image. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%