Low memory block tree coding for hyperspectral images

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

doi:10.1007/s11042-019-07797-6

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Te PSNR is used to assess the performance of image reconstruction. Comprehensively, it can be deduced that the objective (PSNR) and perceptual subjective quality metric (SSIM) show signifcant improvement with compression rates [21,34]. From Table 3, it can be noticed that the 3D-LEZSPChigh-coding gain than other state of art 3D-SPIHT and 3D-NLS as 3D-LEZSPC has more signifcant coefcients than the other compression algorithms.…”

Section: Coding Efciencymentioning

confidence: 91%

“…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%

See 2 more Smart Citations

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: Coding Efciencymentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Transform Based Coding Techniques Te Transformbasedmentioning

confidence: 99%

See 1 more Smart Citation

Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

Bajpai

Sharma²,

Alam

et al. 2023

Journal of Electrical and Computer Engineering

Self Cite

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“…Some state-of-the-art algorithms in this field are 3D-DWT, 8 2D-KLT, 9 3D-set partitioning embedded block (SPECK), 10 and 3D-low memory block tree coding (LMBTC). 11 Technique. Compression using the transform-based method follows some steps that may vary for different algorithms but can be generalized as in Fig.…”

Section: Transform Algorithmsmentioning

confidence: 99%

“…Linear regression is used on spectral coefficients within a cluster, and significant factors are found through least square regression. Memory requirement of techniques is an important issue that is taken care by 3D-LMBTC 11 to encode a higher bit plane to lower bit plane using wavelet coefficients. A block is matched to every other block to find the significance that is encoded in further steps.…”

Section: Transform Algorithmsmentioning

confidence: 99%

Comprehensive review of hyperspectral image compression algorithms

2020

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Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives.

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