• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.129-133
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• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.913-914
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• 2008

This paper presents a method of adaptive image enhancement with dynamic range compression and contrast enhancement. The dynamic range compression is to adaptively enhance the dark area using illumination component of DCT compression block. The contrast enhancement is to modify the image contrast using retinex theory that uses the HVS properties. The block artifacts and other noises, caused by processing in the compression domain, were removed by after processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.185-188
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• 2002

With improved network system and development of computer technology, a lot of hospitals are equipping PACS that deals with process and transmission of the medical images. Owing to equipment of PACS the problems on transmission and storage of the medical images were treated. The way to solve the problems is to use various image processing techniques and compression methods This paper describes RLC in lossless image compression method, JPEG using DCT in loss image compression applied to medical images as way implementing DICOM standard. Now the medical images were compressed with Wavelet transform method have been taken advantage of image process. And compression rate of each compression methods was analyzed.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.288-304
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• 2019

The growth of telemedicine-based wireless communication for images-magnetic resonance imaging (MRI) and computed tomography (CT)-leads to the necessity of learning the concept of image compression. Over the years, the transform based and spatial based compression techniques have attracted many types of researches and achieve better results at the cost of high computational complexity. In order to overcome this, the optimization techniques are considered with the existing image compression techniques. However, it fails to preserve the original content of the diagnostic information and cause artifacts at high compression ratio. In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended with the optimization algorithm to compress the medical images effectively. However, the method becomes time consuming during the measurement of the randomness from the image pixel group and not suitable for medical applications. Hence, an attempt has been made in this paper to develop an HMT based image compression by utilizing the opposition based improved harmony search algorithm (OIHSA) as an optimization technique along with the entropy. Further, the enhancement of the significant information present in the medical images are improved by the proper selection of entropy and the number of thresholds chosen to reconstruct the compressed image.

• Imaging Science in Dentistry
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• v.30 no.4
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• pp.265-274
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• 2000

Purpose: The aims of the present study are to determine the optimum compression rate in terms of file size reduction and diagnostic quality of the images after compression and evaluate the transmission speed of original or each compressed image. Materials and Methods: The material consisted of 24 extracted human premolars and molars. The occlusal surfaces and proximal surfaces of the teeth had a clinical disease spectrum that ranged from sound to varying degrees of fissure discoloration and cavitation. The images from Digora system were exported in TIFF and the images from conventional intraoral film were scanned and digitalized in TIFF by Nikon SF-200 scanner (Nikon, Japan). And six compression factors were chosen and applied on the basis of the results from a pilot study. The total number of images to be assessed were 336. Three radiologists assessed the occlusal and proximal surfaces of the teeth with 5-rank scale. Finally diagnosed as either sound or carious lesion by one expert oral pathologist. And sensitivity, specificity and k value for diagnostic agreement was calculated. Also the area (Az) values under the ROC curve were calculated and paired t-test and oneway ANOVA test was performed. Thereafter, transmission time of the image files of the each compression level was compared with that of the original image files. Results: No significant difference was found between original and the corresponding images up to 7% (1 : 14) compression ratio for both the occlusal and proximal caries (p<0.05). JPEG3 (1 : 14) image files are transmitted fast more than 10 times, maintained diagnostic information in image, compared with original image files. Conclusion: 1 : 14 compressed image file may be used instead of the original image and reduce storage needs and transmission time.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.102-107
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• 2000

In general text compression techniques cannot be used directly in image compression because the model of text and image are different Recently, a new class of text compression, namely, block-sorting algorithm which involves Burrows and Wheeler transformation(BWT) gives excellent results in text compression. However, if we apply it directly into image compression, the result is poor. So, we propose simple method in order to improve the lossless compression performance of image. The proposed method can be divided into three steps. It is decomposed into ten subbands with the help of symmetric short kernel filter. The resulting subbands are block-sorted according to the method by BWT, and the redundancy is removed with the help of an adaptive arithmetic coder. Experimental results show that the proposed method is better than lossless JPEG and LZ-based compression method(PKZIP).

• Imaging Science in Dentistry
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• v.34 no.3
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• pp.117-122
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• 2004

Purpose : To determine the proper image compression method and ratio without image quality degradation in intraoral digital radiographic images, comparing the discrete cosine transform (DCT)-based JPEG with the wavelet-based JPEG 2000 algorithm. Materials and Methods : Thirty extracted sound teeth and thirty extracted teeth with occlusal caries were used for this study. Twenty plaster blocks were made with three teeth each. They were radiographically exposed using CDR sensors (Schick Inc., Long Island, USA). Digital images were compressed to JPEG format, using Adobe Photoshop v.7.0 and JPEG 2000 format using Jasper program with compression ratios of 5 : 1,9 : 1, 14 : 1,28 : 1 each. To evaluate the lesion detectability, receiver operating characteristic (ROC) analysis was performed by the three oral and maxillofacial radiologists. To evaluate the image quality, all the compressed images were assessed subjectively using 5 grades, in comparison to the original uncompressed images. Results: Compressed images up to compression ratio of 14 : 1 in JPEG and 28 : 1 in JPEG 2000 showed nearly the same the lesion detectability as the original images. In the subjective assessment of image quality, images up to compression ratio of 9 : 1 in JPEG and 14 : 1 in JPEG 2000 showed minute mean paired differences from the original Images. Conclusion : The results showed that the clinically acceptable compression ratios were up to 9 : 1 for JPEG and 14 : 1 for JPEG 2000. The wavelet-based JPEG 2000 is a better compression method, comparing to DCT-based JPEG for intraoral digital radiographic images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3823-3840
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• 2020

Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.423-430
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• 2002

When JPEG, a standard of stopped image compression, is high compressed, the image is severely blocked. Since JPEG performs compression after taking DCT(Discrete Cosine Transform). It has a defect that the quality of image becomes low with aliasing in the case of high compression. Though transformation cipher method can have high compression rate, flame nay happen to quality of image by transformation and reverse transformation. In this paper, we use wavelet transform and fractal theory in order to solve these problems. After we apply these two methods to stopped image, we can get some good results, improvement of speed and compression rate, and elimination of blocking appearance. Besides, we show quality of restoration image is better than established one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5056-5078
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• 2018

In this work we propose a compression technique that makes use of linear and perceptually uniform CIE $La^*b^*$ color space in the JPEG image compression framework to improve its performance at lower bitrates. To generate quantization matrices suitable for the linear and perceptually uniform CIE $La^*b^*$ color space, a novel linear Contrast Sensitivity Function (CSF) is used. The compression performance in terms of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), is further improved by utilizing image dependent, variable and non-uniform image sub-blocks generated using a proposed histogram-based merging technique. Experimental results indicate that the proposed linear CSF based quantization technique yields, on an average, 8% increase in CR for the same reconstructed image quality in terms of PSNR as compared to the conventional YCbCr color space. The proposed scheme also outperforms JPEG in terms of CR by an average of 45.01% for the same reconstructed image quality.