• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3245-3271
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• 2016

To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems. At first, this paper proposes a novel image dehazing method by using a new method of calculating the transmission function—namely, the direct denoising method. Next, we deduce the dehazing effects of the direct denoising method and image dehazing method based on dark channel prior (DCP) on image compression in terms of ringing artifacts and blocking artifacts. It can be concluded that the direct denoising method performs better than the DCP method for decompressed (reconstructed) images. We also improve the direct denoising method to obtain more desirable dehazed images with higher contrast, using the saliency map as the guidance image to modify the transmission function. Finally, we adjust the parameters of dehazing contrast-enhancement filters to obtain a corresponding composite peak signal-to-noise ratio (CPSNR) and blind image quality assessment (BIQA) of the decompressed images. Experimental results show that different filters have different effects on image compression. Moreover, our proposed dehazing method can strike a balance between image dehazing and image compression.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.29-38
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• 2022

Although image compression is one of the essential technologies to transmit image data on a variety of surveillance and mobile healthcare applications, it causes unnecessary compression artifacts such as blocking and ringing artifacts by the lossy compression in the limited network bandwidth. Recently, image restoration methods using convolutional neural network (CNN) show the significant improvement of image quality from the compressed images. In this paper, we propose Image Denoising Convolutional Neural Networks (IDCNN) to reduce the compression artifacts for the purpose of improving the performance of object classification. In order to evaluate the classification accuracy, we used the ImageNet test dataset consisting of 50,000 natural images and measured the classification performance in terms of Top-1 and Top-5 accuracy. Experimental results show that the proposed IDCNN can improve Top-1 and Top-5 accuracy as high as 2.46% and 2.42%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2693-2700
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• 2009

This paper proposes an effective compression method to utilize the 3D integral image with large amount of data obtained by a lens array in various applications. The conventional compression methods for still images exhibit low performance in terms of coding efficiency and visual quality, since they cannot remove the correlation between elemental images. In the moving picture compression methods, 1D scanning techniques that produce a sequence of elemental images are not enough to remove the directional correlation between elemental images. The proposed method effectively sequences the elemental images from an integral image by the 2D referencing technique and compresses them using the multi-frame referencing of H.264/AVC. The proposed 2D referencing technique selects the optimal reference image according to vertical, horizontal, and diagonal correlation between elemental images. Experimental results show that compression with the sequence of elemental images presents better coding efficiency than that of still image compression. Moreover, the proposed 2D referencing technique is superior to the 1D scanning methods in terms of the objective performance and visual quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.1003-1008
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• 2013

This paper proposes a compression scheme based on the modified reversible integer transform (MRIT) and forward adaptive prediction for lossless image compression. JPEG XR is the newest image coding standard with high compression ratio and that composed of the Photo Core Transform (PCT) and backward adaptive prediction. To improve the efficiency and quality of compression, we substitutes the PCT and backward adaptive prediction for the modified reversible integer transform (MRIT) and forward adaptive prediction, respectively. Experimental results indicate that the proposed method are superior to the previous method of JPEG XR in terms of lossless compression efficiency and computational complexity.

• 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 Electrical Engineering and information Science
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• v.2 no.6
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• pp.36-42
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• 1997

This paper presents a new design technique for performing high quality low bit rate image compression. A hybrid coder(HC) which combines Mean Removed Important Coefficient Selection based JPEG(MR-ICS-JPEG) and Adaptive Vector Quantization (AVQ) is proposed. A new quantization table is developed using the Important Coefficient Selection(ICS) method; the importance of each coefficient is determined using the orthonormal property of the DCT. This quantization table is applied to standard JPEG with mean removal(MR) strategy before processing. This scheme, called MR-ICS-JPEG, produces more than 2 dB enhanced performance in terms of PSNR over standard JPEG. A set of homogeneous codebooks is generated by homogeneous training vectors. Before compression, an image is uniformly divided into 8${\times}$8 blocks. Low detail regions such as backgrounds are roughly coded by AVQ while high detail regions such as edges or curves are finely coded by the proposed MR-ICS-JPEG. This hybrid coder procuces consistently about 3 dB improved performance in terms of PSNR over standard JPEG.

• 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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2315-2318
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• 2003

The Wavelet Transform has been applied in mathematics and computer sciences. Numerous studies have proven its advantages in image processing and data compression, and have made it a basic encoding technique in data compression standards like JPEG2000 and MPEG-4. Software implementations of the Discrete Wavelet Transform (DWT) appears to be the performance bottleneck in real-time systems in terms of performance. And hardware implementations are not flexible. Therefore, FPGA implementations of the DWT has been a topic of recent research. The goal of this thesis is to investigate of FPGA implementations of the DWT Processor for image compression applications. The DWT processor design is based on the Lifting Based Wavelet Transform Scheme, which is a fast implementation of the DWT The design uses various techniques. The DWT Processor was simulated and implemented in a FLEX FPGA platform of Altera

• Journal of the Korean Society of Industry Convergence
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• v.3 no.4
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• pp.369-378
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• 2000

Conventional fractal image compression methods have many problems in searching time for matching domain block. Proposed method is an improved method of Fisher's Quadtree Decomposition in terms of time, compression ratio, and PSNR. This method determines range block in advance using QR algorithm. First, input image is partitioned to $4{\times}4$ range block and then recomposition is performed from bottom level to specified level. As a result, this proposed method achieves high encoding and decoding speed, high compression ratio, and high PSNR than Fisher's Quadtree Decomposition method.