A low complexity hyperspectral image compression through 3D set partitioned embedded zero block coding

Bajpai, Shrish; Kidwai, Naimur Rahman; Singh, Harsh Vikram; Singh, Amit Kumar

doi:10.1007/s11042-021-11456-0

Cited by 13 publications

(7 citation statements)

References 81 publications

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“…Te four-level curvelet transform is used to transform the HS image and this transform 3D HS image is converted to the 1D array through Morton mapping [23]. Te coefcients in the 1D array are encoded with the HSICAs.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Te proposed HSICA is compared with the other transformbased HSICA [16][17][18][19][20][21][22][23] [16] 3D-SPIHT [17] 3D-WBTC [18] 3D-LSK [19] 3D-NLS [20] 3D-LMBTC [21] 3D-LCBTC [22] 3D-ZM-SPECK [23] 3D-LEZSPC 3D-SPIHT [17] 3D-WBTC [18] 3D-LSK [19] 3D-NLS [20] 3D-LMBTC [21] 3D-LCBTC [22] 3D-ZM-SPECK [23] 3D-LEZSPC (f ) Figure 1: PSNR (in db) comparison of 3D-LEZSPC with 3D-SPECK [16], 3D-SPIHT [17], 3D-WBTC [18], 3D-LSK [19], 3D-NLS [20], 3D-LMBTC [21], 3D-LCBTC [22], and 3D-ZM-SPECK [23] at various bit rates for the four diferent hyperspectral images. [32,33].…”

Section: Methodsmentioning

confidence: 99%

“…Tese algorithms have low-coding complexity, bit stream embeddedness, low coding memory demand, and high-coding gain than other transform-based HSICAs. Te insignifcant coefcients are represented by the block cube (3D-SPECK, 3D-LSK, and 3D-ZM-SPECK) or tree (3D-SPIHT and 3D-NLS) or block cube tree fashion (3D-WBTC, 3D-LMBTC, and 3D-LCBTC) [16][17][18][19][20][21][22][23].…”

Section: Transform Based Coding Techniques Te Transformbasedmentioning

confidence: 99%

“…Te selection of the aforementioned technique difers from application to application [30]. In the last couple of decades, computational devices generate a lot of data, thus the compression of the data is required before the transmission process [23,31]. markers state marker of coefcient (SMC) and state marker of zerotree (SMZ).…”

Section: Curvelet Transformmentioning

confidence: 99%

“…Continued.Bit rate (bpppb) Computation time 3D-SPECK[16] 3D-SPIHT[17] 3D-WBTC[18] 3D-LSK[19] 3D-NLS[20] 3D-LMBTC[21] 3D-LCBTC[22] 3D-ZM-SPECK[23] …”

mentioning

confidence: 99%

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Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

Bajpai

Sharma²,

Alam

et al. 2023

Journal of Electrical and Computer Engineering

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The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Transform Based Coding Techniques Te Transformbasedmentioning

confidence: 99%

Section: Curvelet Transformmentioning

confidence: 99%

“…Continued.Bit rate (bpppb) Computation time 3D-SPECK[16] 3D-SPIHT[17] 3D-WBTC[18] 3D-LSK[19] 3D-NLS[20] 3D-LMBTC[21] 3D-LCBTC[22] 3D-ZM-SPECK[23] …”

mentioning

confidence: 99%

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