• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.825-828
• /
• 2022

In this paper, we propose an efficient image size selection method for video-based point cloud compression. The current MPEG video-based point cloud compression reference encoding process configures a threshold on the size of images while converting point cloud data into images. Because the converted image is compressed and restored by the legacy video codec, the size of the image is one of the main components in influencing the compression efficiency. If the image size can be made smaller than the image size determined by the threshold, compression efficiency can be improved. Here, we studied how to improve the compression efficiency by selecting the best-fit image size generated during video-based point cloud compression. Experimental results show that the proposed method can reduce the encoding time by 6 percent without loss of coding performance compared to the test model 15.0 version of video-based point cloud encoder.

• Smart Structures and Systems
• /
• v.24 no.5
• /
• pp.659-668
• /
• 2019

In digital image correlation, speckle image is closely related to the measurement accuracy. A practical global evaluation criterion for speckle image is presented. Firstly, based on the essential factors of the texture image, both the average particle size and image sharpness are used for the assessment of speckle image. The former is calculated by a simplified auto-covariance function and Gaussian fitting, and the latter by focusing function. Secondly, the computation of the average particle size and image sharpness is verified by numerical simulation. The influence of these two evaluation parameters on mean deviation and standard deviation is discussed. Then, a physical model from speckle projection to image acquisition is established. The two evaluation parameters can be mapped to the physical devices, which demonstrate that the proposed evaluation method is reasonable. Finally, the engineering application of the evaluation method is pointed out.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3642-3667
• /
• 2021

Texture synthesis technology has the advantages of repairing texture and structure at the same time. However, during the filling process, the size of the patch is fixed, and the content of the filling is not fully considered. In order to be able to adaptively change the patch size, we used the exemplar-based inpainting technique as the test algorithm, considering the image structure and texture, calculated the image structure patch size and texture patch size, and comprehensively determined the image patch size. This can adaptively change the patch size according to the filling content. In addition, we use multi-layer images to calculate the priority, so that the order of image repair was more stable. The proposed repair algorithm is compared with other image repair algorithms. The experimental results showed that the proposed adaptive image repair algorithm can better repair the texture and structure of the image, which proved the effectiveness of the proposed algorithm.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.561-564
• /
• 2005

In this paper, we present a new JPEG (Joint Photograph Experts Group) compression architecture which compresses still image into fixed size of bitstream to use restricted system memory efficiently. The size of bitstream is determined by the complexity of image and the quantization table. But the quantization table is set in advance the complexity of image is the essential factor. Therefore the size of bitstream for high complexity image is large and the size for low complexity image is small. This means that the management of restricted system memory is difficult. The proposed JPEG encoder estimates the size of bitstream using the correlation between consecutive frames and selects the quantization table suited to the complexity of image. This makes efficient use of system memory.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.187-190
• /
• 2007

This study is for analyzing best band in image matching using correlation coefficient of left and right images of stereo image pair, lot red, green, blue band images separated from color aerial photo and gray image converted from the same color aerial photo image. The image matching is applied to construct Digital Elevation Model(DEM) or terrain data. The correlation coefficients and variation by change of pixel patch size are computed from pixel patches of which sizes are $11{\times}11{\sim}101{\times}101$. Consequently, the correlation coefficient in red band image is highest. The lowest is in blue band. Therefore, to construct terrain data using image matching, the red band image is preferable. As the size of pixel patch is growing, the correlation coefficient is increasing. But increasing rate declines from $51{\times}51$ image patch size and above. It is proved that the smaller pixel patch size than $51{\times}51$ is applied to construct terrain data using image matching.

• Journal of the Korean Society of Costume
• /
• v.58 no.6
• /
• pp.54-68
• /
• 2008

The purpose of this study is to investigate the effect of dot pattern size(0.8, 1.8, 2.5, 5, 8), color combination(BG/R, Y/B), area-ratio on image formation. Sets of stimulus and response scales(7 point semantic) were used as experimental materials. The stimuli were 20 color pictures manipulated with the combination of dot pattern size, color combination, and area-ratio using computer simulation. The subjects were 240 female undergraduates living in Gyeongnam-do. Image factor of the stimulus was composed of 5 different components, visibility, attractiveness, cuteness, stability and high class image. In the cuteness, color combination, dot pattern size showed independent effect. In the stability, area-ratio, dot pattern size showed independent effect. Interaction effects of color and area-ratio combination was significant on cuteness. For visibility image 8cm yellow dot/blue background, for attractiveness image BG/R coloration, for cuteness image Y/B coloration and for stability image 0.8cm yellow dot/blue background were effective. According to the variation of dot pattern size, color combination and area-ratio, it was investigated that the images for a dress wearer were expressed diversely, were shown differently in image dimensions, and could be produced to different images.

• Journal of the Korean Society of Radiology
• /
• v.11 no.6
• /
• pp.443-451
• /
• 2017

The purpose of this study was to comparison of measure signal to noise ratio (SNR) according to change of kernel size from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 100 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p<0.05). In SNR results, with the quality of distributions in the order of kernel size 9*9 image, kernel size 7*7 image and original chest image, kernel size 3*3 image (p<0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the kernel size chest image.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4326-4344
• /
• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.4
• /
• pp.1790-1806
• /
• 2016

Digital image encryption is widely used for image data security. JPEG standard compresses image with great performance on reducing file size. Thus, to encrypt an image in JPEG format we should keep the quality of original image and reduced size. This paper proposes a JPEG image encryption scheme based on quantized DC and non-zero AC coefficients inner category scrambling. Instead of coefficient value encryption, the address of coefficient is encrypted to get the address of cipher text. Then 8*8 blocks are shuffled. Chaotic iteration is employed to generate chaotic sequences for address scrambling and block shuffling. Analysis of simulation shows the proposed scheme is resistant to common attacks. Moreover, the proposed method keeps the file size of the encrypted image in an acceptable range compared with the plain text. To enlarge the cipher text possible space and improve the resistance to sophisticated attacks, several additional procedures are further developed. Contrast experiments verify these procedures can refine the proposed scheme and achieve significant improvements.

• Journal of Information Processing Systems
• /
• v.13 no.2
• /
• pp.305-320
• /
• 2017

The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (${\eta}$) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.