Compression efficiency for combining different embedded image compression techniques with Huffman encoding

Srikanth, Sure; Meher, Sukadev

doi:10.1109/iccsp.2013.6577170

Cited by 26 publications

(14 citation statements)

References 10 publications

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“…Guo et al utilized Discrete Wavelet Transform (DWT), a lossless wavelet transform in conjunction with Huffman encoder, and achieved higher compression. DWT for lossless compression has been utilized in methodologies proposed by Srikanth and Meher in [31] and Mozammel et al in [32]. Telagarapu et al in [33] employed Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) showing that the DWT outperformed the DCT in terms of lossless compression.…”

Section: Reversible Colour Transforms (Rcts)mentioning

confidence: 99%

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

Waleed

Khan

et al. 2020

Applied Sciences

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Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows–Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.

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Section: Reversible Colour Transforms (Rcts)mentioning

confidence: 99%

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

Waleed

Khan

et al. 2020

Applied Sciences

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“…Srikanth S. et al [14] implemented the SPIHT and EZW algorithms along with Huffman encoding by utilizing various wavelet families and comparing the PSNRs as well as bit rates of these wavelet families. This algorithm was carried out on various images and showed that the result of the proposed methodology were better as they provided good compression ratio and high image quality when compared with the traditional methods.…”

Section: Related Workmentioning

confidence: 99%

Review of Various Techniques for Medical Image Compression

Kaur¹,

Kaur²,

Kumar³

2015

IJCA

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Image Compression generally refers to reducing the size of an image for the purpose of minimizing storage space as well as reducing the transfer time when transmitted over the network. Compression is very useful in medical imaging as a large quantity of storage is needed for storing medical images which can further be delivered for diagnosis. An appropriate technique for compression is needed for saving storage capacity as well as network bandwidth. It is also necessary that the valuable information should not be lost after compression of an image. In this paper various techniques of lossless image compression for medical images have been reviewed. The evaluation of performance is based on the parameter compression ratio.

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“…In Ref. [13] digital image compression which uses the unique embedded Wavelet based image coding in combination with Huffman-encoder for more compression. In [14] they proposed a lossless image compression technique combining a unique step with the integer wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

“…Bit per pixel in the case of decoded image can be calculated by obtaining the total number of bits of the image i.e. number of significant coefficients multiplied by 8 and after this dividing them by total number of coefficients (or total number of pixels) [13].…”

Section: Bit Per Pixel (Bpp)mentioning

confidence: 99%