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Hybrid Structural and Textural Analysis for Efficient Image Compression

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Abstract

In recent trends, the image compression plays a pivotal part in conveying information throughout the world. It helps to reduce the redundant data of the image for the purpose of storing as well transmission in a cost effective manner. There are several conventional techniques that are used for image compression, but still it lacks in some issues such as increased error, inaccurate results, ineffective, etc. Thus, a novel hybrid image compression approach is proposed in this work for better image compression. Initially the input image is decomposed as structural and textural regions of the image. From the decomposed image, the process of feature extraction is carried out. Here the features from structural regions are extracted using Partial Differential Equation (PDE) based inpainting approach. The features from the textural features are extracted using the texture based algorithm. Among these extracted features, the redundant details are dropped to preserve the significant details. From this, the compressed image is obtained at the receiver side, the significant details from the compressed image performs region completion operation. Here the holes in structural regions are filled by PDE based interpolation technique and the textural regions are filled using texture algorithm, also the color information is synthesized along with this textural detail. Finally, the inpainting algorithm is used for correcting the discontinuities. The experimental results prove the superiority of this hybrid image compression.

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This research work was not funded by any organization/institute/agency.

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Authors and Affiliations

  1. Research Scholar, Anna University, Chennai, India

    B. Vidhya

  2. Biomedical Engineering Department, PSG College of Technology, Coimbatore, India

    R. Vidhyapriya

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  1. B. Vidhya
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  2. R. Vidhyapriya
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Correspondence to B. Vidhya.

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Vidhya, B., Vidhyapriya, R. Hybrid Structural and Textural Analysis for Efficient Image Compression. Wireless Pers Commun 120, 2831–2845 (2021). https://doi.org/10.1007/s11277-021-08587-w

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