Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

Khalaf, Walaa; Zaghar, Dhafer R.; Hashim, Noor Hazarina

doi:10.3390/sym11020291

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…Y, where X and Y are image dimensions. In addition, Structural Similarity Index (SSIM) is used as a subjective quality measurement for the test images besides the PSNR, SSIM value ranges between 0.0-1.0, where low value means large structural variation, and vice versa [11,22]. Four tests are carried out to show the improvements of the proposed method compared to JPEG compression.…”

Section: Resultsmentioning

confidence: 99%

“…Image compression is implemented into spatial domain and frequency domain. In spatial domain, image compression techniques aim to reduce the number of pixels representing the image without influencing the quality of the resulted image [9][10][11]. In frequency domain, Discrete Cosine Transform (DCT) [12,13], Discrete Fourier transform, or Discrete Wavelet Transform [5,14,15] are used to represent the energy of the image into a few number of coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…For each four points, construct a 2 × 2 matrix, then the tanh function presented in [11] is used to estimate a 2 × 4 matrix from the resulting image in step 1 as shown in Figure 2b:…”

mentioning

confidence: 99%

“…For each column of the 2 × 4 matrix, the tanh function presented in [11] is reimplemented to estimate the other points for constructing the decompressed 4 × 4 blocks as shown in Figure 2c:…”

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confidence: 99%

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Pre and Postprocessing for JPEG to Handle Large Monochrome Images

2019

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Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For each four points, construct a 2 × 2 matrix, then the tanh function presented in [11] is used to estimate a 2 × 4 matrix from the resulting image in step 1 as shown in Figure 2b:…”

mentioning

confidence: 99%

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confidence: 99%

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Pre and Postprocessing for JPEG to Handle Large Monochrome Images

2019

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“…Evaluation indexes: There are many indexes to evaluate the image quality of the reconstructed images, e.g., qualitative [40,41] and quantitative [42][43][44][45]. In this paper, six quantitative indexes were employed to evaluate the quality of the reconstructed HSIs.…”

Section: Competitors and Evaluation Indexesmentioning

confidence: 99%

Domain Transfer Learning for Hyperspectral Image Super-Resolution

Zhang

You

2019

Remote Sensing

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A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.

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