Digital Image Compression using Hybrid Transform with Kekre Transform and Other Orthogonal Transforms

Kekre, H. B.; Sarode, Tanuja; Natu, Prachi

doi:10.9790/0661-16133846

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Within this domain, the integration of hybrid wavelet transform techniques has emerged as a promising approach, offering enhanced accuracy and robustness. [6][7] H. B. Kekre, Archana Athawale, and Dipali Sadavarti introduced an algorithm for generating a discrete wavelet transform employing orthogonal transformation. In this algorithm, for an M × M orthogonal transform matrix P, each element of every row is replicated M times to produce M mother wavelets.…”

Section: Reviewmentioning

confidence: 99%

“…Their investigation revealed that the DKT-DCT hybrid wavelet transform demonstrated robustness against various attacks, including compression, cropping, noise addition, and resizing, outperforming both DCT and DKT individual transforms. [15] Handwritten online signature verification is an important aspect of modern authentication systems, ensuring secure and reliable access to sensitive information. Within this domain, the integration of hybrid wavelet transform techniques has emerged as a promising approach, offering enhanced accuracy and robustness.…”

Section: Reviewmentioning

confidence: 99%

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Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Manoj Chavan

2024

jes

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Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

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Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Manoj Chavan

2024

jes

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“…In the process of decoding, the high frequency section carries out scathe less predicting decoding, while the low frequency section is decoded to iterative function system code which is used for subimage reconstruction. Compression using hybrid transform is presented in [13]. Kronecker product of Kekre transform with other orthogonal transforms is used and error at different compression ratios is observed.…”

Section: Review Of Literaturementioning

confidence: 99%

Color Image Compression using hybrid Haar-DCT wavelet in Different color spaces

Kekre¹,

Sarode²,

Natu

2014

AIVP

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This paper compares digital image compression in various color spaces using Hybrid Haar wavelet transform. As DCT has high energy compaction property, it is combined with Haar transform generating Haar-DCT hybrid wavelet transform. Different component sizes are used and error at different compression ratios up to 32 is observed. At higher compression ratios 16-16 component pair gives minimum error. This pair is further used to compress images in different color spaces like RGB, KLUV, YUV, YIQ, XYZ and YCbCr. KLUV color space show considerably less Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than other color spaces. Average Fractional Change in Pixel Value (AFCPV) in KLUV color space is nearly half than other color spaces. RMSE, MAE these measures are not sufficient as it give perceived error to human visual system. Hence other error metric Structural Similarity Index (SSIM) is used to observe performance superiority of various color spaces. SSIM in KLUV and RGB color space is equal having value 0.994 which is closest to one indicating better but similar image quality in both color spaces. This observation cannot be emphasized using traditional error metrics like RMSE, MAE and AFCPV.

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“…Within this domain, the integration of hybrid wavelet transform techniques has emerged as a promising approach, offering enhanced accuracy and robustness. [6][7] H. B. Kekre, Archana Athawale, and Dipali Sadavarti introduced a novel algorithm for creating a discrete wavelet transform through orthogonal transformation. Their approach involves utilizing an M × M orthogonal transformation matrix, denoted as P, where each element in every row of P is replicated M times to produce M mother wavelets.…”

mentioning

confidence: 99%

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Manoj Chavan

2024

jes

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verification and forgery detection. This feature vector is given to the Left-Right and Ergodic modelled HMM classifier for analysis. HMM is trained using 10 randomly chosen genuine signature samples and is used to test remaining 10 genuine signatures and 20 forged signatures of 40 users of SVC 2004 signature database. This process is iterated 20 times and then average values are calculated. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Left Right HMM model, DCT 4 Haar 64 HWT-1 offers best performance of FRR, FAR of 1.05%, 0.99% respectively for state 3. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Ergodic HMM model, DCT 4 DHT 64 offers best performance of FRR, FAR of 1.10%, 2.88% respectively for state 5. We conclude that comparing HWT-1 and HWT-2 combinations for Left Right and Ergodic HMM model, HWT-1 for Left Right model offer better performance.

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Digital Image Compression using Hybrid Transform with Kekre Transform and Other Orthogonal Transforms

Abstract: 8-32,16-16 and 64-4.

Cited by 5 publications

References 14 publications

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Color Image Compression using hybrid Haar-DCT wavelet in Different color spaces

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

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