scholarly journals Pre and Postprocessing for JPEG to Handle Large Monochrome Images

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 255 ◽  
Author(s):  
Walaa Khalaf ◽  
Abeer Al Gburi ◽  
Dhafer Zaghar
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Reconstructed Image ◽  
Hyperbolic Function ◽  
Image Size ◽  
New Techniques ◽  
Large Size

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.

scholarly journals Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 291 ◽  
Author(s):  
Walaa Khalaf ◽  
Dhafer Zaghar ◽  
Noor Hashim
Keyword(s):  
Image Compression ◽  
Curve Fitting ◽  
Image Data ◽  
Structural Similarity ◽  
Blocking Effect ◽  
Reconstructed Image ◽  
Hyperbolic Function ◽  
Image Size ◽  
Hyperbolic Tangent ◽  
Order Curve

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.

scholarly journals Image Compression Based on Arithmetic Coding Algorithm

2021 ◽  
pp. 329-334
Author(s):  
Ruaa Ibrahim Yousif ◽  
Nassir Hussein Salman
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Rapid Development ◽  
Arithmetic Coding ◽  
Compact Form ◽  
Color Model ◽  
Cosine Transform ◽  
High Image Quality ◽  
The Past

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

scholarly journals Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device

2021 ◽  
Vol 11 (14) ◽  
pp. 6277
Author(s):  
Takayuki Takahashi ◽  
Tomoyoshi Shimobaba ◽  
Takashi Kakue ◽  
Tomoyoshi Ito
Keyword(s):  
Image Quality ◽  
High Speed ◽  
Reconstructed Image ◽  
Digital Micromirror Device ◽  
Full Color ◽  
Large Size ◽  
Reconstruction Image ◽  
Reduction Methods ◽  
Time Division

Holographic projection is a simple projection as it enlarges or reduces reconstructed images without using a zoom lens. However, one major problem associated with this projection is the deterioration of image quality as the reconstructed image enlarges. In this paper, we propose a time-division holographic projection, in which the original image is divided into blocks and the holograms of each block are calculated. Using a digital micromirror device (DMD), the holograms were projected at high speed to obtain the entire reconstructed image. However, the holograms on the DMD need to be binarized, thereby causing uneven brightness between the divided blocks. We correct this by controlling the displaying time of each hologram. Additionally, combining both the proposed and noise reduction methods, the image quality of the reconstructed image was improved. Results from the simulation and optical reconstructions show we obtained a full-color reconstruction image with reduced noise and uneven brightness.

scholarly journals Time-division Color Holographic Projection in Large Size Using a Digital Micromirror Device

2021 ◽  
Author(s):  
Takayuki Takahashi ◽  
Tomoyoshi Shimobaba ◽  
Takashi Kakue ◽  
Tomoyoshi Ito
Keyword(s):  
Image Quality ◽  
High Speed ◽  
Reconstructed Image ◽  
Digital Micromirror Device ◽  
Full Color ◽  
Large Size ◽  
Reconstruction Image ◽  
Reduction Methods ◽  
Time Division

Holographic projection is a simple projection because it enlarges or reduces reconstructed images without using a zoom lens. However, one major problem associated with this projection is the deterioration of image quality as the reconstructed image enlarges. In this paper, we propose a time-division holographic projection, in which the original image is divided into blocks and the holograms of each block are calculated. Using a digital micromirror device (DMD), the holograms were projected at high speed to obtain the entire reconstructed image. However, the holograms on the DMD need to be binarized, thereby causing uneven brightness between the divided blocks. We correct this by controlling the displaying time of each hologram. Additionally, combining both the proposed and noise reduction methods, the image quality of the reconstructed image was improved. Results from the simulation and optical reconstructions show we obtained a full-color reconstruction image with reduced noise and uneven brightness.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Method for Improving Reconstructed Image Quality of Digital Hologram Based on SRAD and NSCT

2014 ◽  
Vol 41 (2) ◽  
pp. 0209024
Author(s):  
吴一全 Wu Yiquan ◽  
殷骏 Yin Jun ◽  
朱丽 Zhu Li ◽  
叶志龙 Ye Zhilong
Keyword(s):  
Image Quality ◽  
Reconstructed Image ◽  
Digital Hologram

Diagnosis of glaucoma using telemedicine – the effect of compression on the evaluation of optic nerve head cup–disc ratio

2000 ◽  
Vol 6 (1_suppl) ◽  
pp. 123-125 ◽  
Author(s):  
Danielle Beauregard ◽  
John Lewis ◽  
Marc Piccolo ◽  
Harold Bedell
Keyword(s):  
Optic Nerve ◽  
Image Quality ◽  
Optic Nerve Head ◽  
Compression Ratio ◽  
Optical Resolution ◽  
Jpeg Images ◽  
Disk Space ◽  
Blinded Observer ◽  
Tiff Format

A photograph of the optic nerve head requires a lot of disk space (over 1 MByte) for storage and may require substantial bandwidth and time for transmission to a remote practitioner for a second opinion. To test whether compression degrades the image quality of the images, 302 slides were digitized at an optical resolution of 2400 pixels/inch (945 pixels/cm) and 30 bit/pixel. The images were saved both in non-compressed TIFF format and in compressed JPEG (compression ratio of 60) format. A blinded observer measured the optic nerve head cup–disc ratio for all three groups: the original slides, uncompressed TIFF and compressed JPEG images. The results showed that digital images were less accurate than slides. However, compression, even up to a ratio of 40, did not make matters worse.

Image Compression Algorithm with Radix-4 FFT

2010 ◽  
Vol 143-144 ◽  
pp. 404-408
Author(s):  
Jia Bin Deng ◽  
Juan Li Hu ◽  
He Hua Chi ◽  
Jue Bo Wu
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Image Compression ◽  
Compression Ratio ◽  
Conformal Symmetry ◽  
Research Focus ◽  
Data Store ◽  
Long Time ◽  
Image Compression Algorithm

Image compression technology has been the research focus in the field of image processing all the time. In this paper, Radix-4 FFT is introduced to realize limit distortion coding of image. The presented method aims to solve the problems of Fourier transform on existing complexity and long time-consuming, and it can reduce the number of data store by conformal symmetry of Fourier transform. Using Radix-4 FFT, the time-consuming can be highly shortened and two different kinds of quantization tables are designed according to image compression ratio and the quality of image.

scholarly journals DESIGN OF NEURAL NETWORK AS DATA FLOW MODEL FOR IMAGE COMPRESSION

2013 ◽  
pp. 195-199
Author(s):  
DANESHWARI I. HATTI ◽  
SAVITRI RAJU ◽  
MAHENDRA M. DIXIT
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Convergence Time ◽  
Training Algorithms ◽  
Hardware Complexity ◽  
Cosine Transform ◽  
Hidden Neurons

In digital communication bandwidth is essential parameter to be considered. Transmission and storage of images requires lot of memory in order to use bandwidth efficiently neural network and Discrete cosine transform together are used in this paper to compress images. Artificial neural network gives fixed compression ratio for any images results in fixed usage of memory and bandwidth. In this paper multi-layer feedforward neural network has been employed to achieve image compression. The proposed technique divides the original image in to several blocks and applies Discrete Cosine Transform (DCT) to these blocks as a pre-process technique. Quality of image is noticed with change in training algorithms, convergence time to attain desired mean square error. Compression ratio and PSNR in dB is calculated by varying hidden neurons. The proposed work is designed using MATLAB 7.10. and synthesized by mapping on Vertex 5 in Xilinx ISE for understanding hardware complexity. Keywords - backpropagation, Discrete

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