Image compression using lifting based wavelet transform coupled with SPIHT algorithm

Kabir, Md. Ahasan; Khan, Aslam; Islam, Tajul; Hossain, Liton; Mitul, Abu Farzan

doi:10.1109/iciev.2013.6572638

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…The wavelet transform creates a floating-point coefficient, which helps to compress the image significantly [30]. Although these coefficients are sufficient to reconstruct the original image, the quantization of the coefficient using finite arithmetic precision turns the process into lossy.…”

Section: Wavelet Transformationmentioning

confidence: 99%

An efficient low bit rate image watermarking and tamper detection for image authentication

Kabir

2021

SN Appl. Sci.

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This study presents the vulnerability of digital documents and its effective way to protect the ownership and detection of unauthorized modification of multimedia data. Watermarking is an effective way to protect vulnerable data in a digital environment. In this paper, a watermarking algorithm has been proposed based on a lossy compression algorithm to ensure authentication and detection of forgery. In this proposed method, the CDF9/7 biorthogonal wavelet is used to transform the watermark image and encoded the wavelet coefficients using Set Partition in Hierarchical Tree algorithm. Then, the encoded bits are encrypted by shuffling and encrypting using symmetric keys. After that the encrypted bits are inserted into the Least Significant Bit position of the cover image. In addition, two tamper detection bits are generated based on texture information and pixel location and inserted in the watermarked image. The proposed algorithm reconstructs the watermark and the tampering region more efficiently and achieved 56.5463 dB PSNR for STARE database. Experimental result shows that the proposed algorithm is effectively prevented different attacks and ensure the integrity of watermark bits within the watermarked image. Also finds the tampered region more efficiently compared with the existing state of art algorithms.

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Section: Wavelet Transformationmentioning

confidence: 99%

An efficient low bit rate image watermarking and tamper detection for image authentication

Kabir

2021

SN Appl. Sci.

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“…In addition to the storage, data are often required to be transmitted over the Internet at the highest possible speed. Due to the constraint in storage facility and limitation in transmission bandwidth, compression of data is vital [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Kabir

Mondal

2018

J. Imaging

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Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an "edge-based transformation and entropy coding" (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed "prediction-based transformation and entropy coding" (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: "joint photographic experts group lossless" (JPEG-LS), "set partitioning in hierarchical trees" (SPIHT) and "differential pulse code modulation" (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.compression are some of the wavelet-based compressions such as embedded zerotrees of wavelet transforms (EZW), joint photographic experts group (JPEG) and the moving picture experts group (MPEG) compression.A large number of research papers report image compression algorithms. For example, one study [13] is about discrete cosine transform (DCT)-based lossless image compression where the higher energy coefficients in each block are quantized. Next, an inverse DCT is performed only on the quantized coefficients. The resultant pixel values are in the 2-D spatial domain. The pixel values of two neighboring regions are then subtracted to obtain residual error sequence. The error sequence is encoded by an entropy coder such as Arithmetic or Huffman coding [13]. Image compression in the frequency domain using wavelets is reported in several studies [12,[14][15][16][17]]. In the method described in [14] lifting-based bi-orthogonal wavelet transform is used which produces coefficients that can be rounded without any loss of data. In the work of [18] wavelet transform limits the image energy within fewer coefficients which are encoded by "set partitioning in hierarchical trees" (SPIHT) algorithm.In [19] JPEG lossless (JPEG-LS), a prediction-based lossless scheme, is proposed for continuous tone images. In [14] embedded zero tree coding (EZW) method is proposed based on the zero tree hypothesis. The study in [12] proposes a compression algorithm based on combination of discrete wavelet transform ...

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“…Cavero et al [14] proposed a colour‐embedded 3D SPIHT codec that retains full embeddedness and precise rate control. Kabir et al [15] proposed an improved SPIHT algorithm based on lifting‐based coupled wavelet transform. Zhong et al .…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional separate descendant‐based SPIHT algorithm for fast compression of high‐resolution medical image sequences

Song

Huang

Chang

et al. 2017

IET Image Processing

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Image compression using lifting based wavelet transform coupled with SPIHT algorithm

Cited by 15 publications

References 4 publications

An efficient low bit rate image watermarking and tamper detection for image authentication

An efficient low bit rate image watermarking and tamper detection for image authentication

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Three‐dimensional separate descendant‐based SPIHT algorithm for fast compression of high‐resolution medical image sequences

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