Volume 14, Issue 2 (9-2017)                   JSDP 2017, 14(2): 97-114 | Back to browse issues page

DOI: 10.18869/acadpub.jsdp.14.2.97

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Shiraz University
Abstract:   (1940 Views)

Compression can be done by lossy or lossless methods. The lossy methods have been used more widely than the lossless compression. Although, many methods for image compression have been proposed yet, the methods using intelligent skipping proper to the visual models has not been considered in the literature. Image inpainting refers to the application of sophisticated algorithms to replace lost or corrupted parts of the data so that visual difference cannot be inferred from the reconstructed image. In this paper, first we review some of the image inpainting algorithms and some of the image compression techniques using the inpainting algorithms, we propose a new inpainting based image compression algorithm that can improve the compression rate considerably. We present image compression system based on the proposed parameter-assistant image inpainting method to more deeply exploit visual redundancy inherent in color images. We have shown that with carefully selected dropped regions and appropriately extracted parameters from them, dropped regions can be satisfactorily restored using the proposed PAI algorithm. Accordingly, our compression scheme has a higher coding performance compared with traditional methods in terms of the perceptual quality. To best represent the target region for inpainting, an effective region classifier is required. A generic solution is to study the distribution of each image region and find the best match among the candidates in the predefined model class. For simplicity, in our scheme, an entire image divided into three categories: gradated, structural, and non-featured, at non-overlapping block level of size S×S. The classification is performed based on edge content and color variance in each block. Simulation results show that our proposed method has reasonable visual quality in comparison with the other proposed image compression algorithms.  

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Type of Study: Research | Subject: Paper
Received: 2015/10/8 | Accepted: 2017/05/20 | Published: 2017/10/21 | ePublished: 2017/10/21

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