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

Kabir, Md. Ahasan; Mondal, M. Rubaiyat Hossain

doi:10.3390/jimaging4050064

Cited by 17 publications

(12 citation statements)

References 39 publications

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“…Lossy image compression methods make use of quantization that can be of many types such as scalar, uniform and dot-division matrix [5]. Our method is based on dot-division matrix, with a quantization matrix n × n containing data generated through Equation 3.…”

Section: The Two-level Dct Methodsmentioning

confidence: 99%

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A Novel Method for Image and Video Compression Based on Two-Level DCT with Hexadata Coding

Siddeq

Rodrigues

2020

Sens Imaging

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In this paper a novel method for 2D image compression is proposed and demonstrated through high quality reconstruction with compression ratios up to 99%. The proposed novel algorithm is based on a two-level Discrete Cosine Transform (DCT) followed by Hexadata coding and arithmetic coding at compression stage. The novel method consists of four main steps:1) A twolevel DCT is applied to an image to reinforce the low frequency coefficients and increase the number of high frequency coefficients to facilitate the compression process; 2) The Hexadata coding algorithm is applied to each high frequency matrix separately through five different keys to reduce each matrix to 1/6 of their original size; 3) Build a probability table of original highfrequency data required in the decoding step; and 4) Apply arithmetic coding to compress each of the outputs of steps (2) and (3). At decompression stage, arithmetic decoding and a Fast Matching Search Algorithm (FMS-Algorithm) decodes the high frequency coefficients of step (2) using the probability table of step (3). Finally, two level inverse DCT is applied to decode the high frequency coefficients to reconstruct the image. The technique is demonstrated on still images including video streaming from YouTube. The results show that the proposed method yields high compression ratios up to 99% with better perceptual quality of reconstructed images as compared with the popular JPEG method.

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Section: The Two-level Dct Methodsmentioning

confidence: 99%

“…Such artefacts significantly reduce the visual quality and clarity of the text, graphics and images when decompressed. Recently, several new coding methods have been developed for image compression [5]. However, the encoding processes of these methods are also ultimately more complex than the JPEG algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Image and Video Compression Based on Two-Level DCT with Hexadata Coding

Siddeq

Rodrigues

2020

Sens Imaging

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“…Its performance is quite better than the existing well-known state of art methods such as JPEG-2000, EZW. It is a progressive coding method, where the wavelet transformed coefficient is considered significant or insignificant based on a threshold [31]. If a particular coefficient of subband has the highest level of value against the threshold is considered as a significant subband otherwise insignificant.…”

Section: Encoding With Spiht Algorithmmentioning

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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“…Furthermore, image compression can be implemented into two domains, namely spatial and frequency. In the spatial domain, the digital image is decomposed into its bit-planes [8], and the process detects how to ignore some of these pixels without affecting the resulting image [9]. In the frequency domain a discrete transform such as Discrete Cosine transform (DCT), Fourier Transform (FT), or Wavelet Transform (WT) that can be applied to compact the energy of the image into only few coefficients, while low coefficients can be set to zero, followed by quantization and entropy coding, such as Huffman coding method, to reduce the number of bits required to represent pixel value (bpp) [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

2019

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Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st-and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image.

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

Cited by 17 publications

References 39 publications

A Novel Method for Image and Video Compression Based on Two-Level DCT with Hexadata Coding

A Novel Method for Image and Video Compression Based on Two-Level DCT with Hexadata Coding

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

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

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