• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.117-126
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• 2012

This paper provides a method for compression transmission of on-chip bus data. As the data traffic on on-chip buses is rapidly increasing with enlarged video resolutions, many video processor chips suffer from a lack of bus bandwidth and their IP cores have to wait for a longer time to get a bus grant. In multimedia data such as images and video, the adjacent data signals very often have little or no difference between them. Taking advantage of this point, this paper develops a simple bus data compression method to improve the chip performance and presents its hardware implementation. The method is applied to a Video Codec - 1 (VC-1) decoder chip and reduces the processing time of one macro-block by 13.6% and 10.3% for SD and HD videos, respectively

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.140-146
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• 2023

A detailed survey, applications and challenges of video encoding-decoding systems is discussed in this paper. A novel architecture has also been set aside for future work in the same direction. The literature reviews span the years 1960 to the present, highlighting the benchmark methods proposed by notable academics in the field of video compression. The timeline used to illustrate the review is divided into three sections. Classical methods, conventional heuristic methods, and current deep learning algorithms are all used for video compression in these categories. The milestone contributions are discussed for each category. The methods are summarized in various tables, along with their benefits and drawbacks. The summary also includes some comments regarding specific approaches. Existing studies' shortcomings are thoroughly described, allowing potential researchers to plot a course for future research. Finally, a closing note is made, as well as future work in the same direction.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.192-194
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• 2005

Acceptance of the international standards for video compression, such as H.261, MPEG-1 and MPEG-2, along with the developments in video codec hardware, has created an explosion of application. Among these, the long time quest for long-distance digital video transmission causes an increasing interest in transporting compressed video over networks which are nontraditional for this purpose, including asynchronous transfer mode networks, the Internet, and cellular and wireless channels. Transmission of compression video over packet network is improved for error resilience. And layered video coding techniques improves error resilience. We present a efficient method of scalable video coding for low bandwidth.

• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.141-146
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• 1999

In this paper, picture set filter is proposed for preserving compression ratio and video qualify. This filter controls the compression ratio of each frame depending on the correlation to the reference frame by selectively eliminating less important high-resolution areas. Consequently, video quality can be preserved and bit rate can be controlled adaptively. In the simulation, to test the performance of the proposed coding method, comparisons with the full search block matching algorithm and the differential image coding algorithm are made. In the former case, video quality, compression ratio and encoding time is improved. In the latter case, video quality is degraded, but compression ratio and encoding time is improved. Consequently. the proposed method shows a reasonably good performance over existing ones.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.43-51
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• 2014

Multi-view video including depth image is necessary to develop a new compression encoding technique for storage and transmission, because of a huge amount of data. Layered depth image is an efficient representation method of multi-view video data. This method makes a data structure that is synthesis of multi-view color and depth image. This efficient method to compress new contents is suggested to use layered depth image representation and to apply for video compression encoding by using 3D warping. This paper proposed enhanced compression method using layered depth image representation and H.264/AVC video coding technology. In experimental results, we confirmed high compression performance and good quality of reconstructed image.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.61-67
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• 2011

In this paper, a new method for effective video coding is studied. Picture set filter is proposed for preserving compression ratio and video quality. This filter controls the compression ratio of each frame depending on the correlation to the reference frame by selectively eliminating less important high-resolution areas. Consequently, video quality can be preserved and bit rate can be controlled adaptively. In the simulation, to test the performance of the proposed coding method, comparisons with the full search block matching algorithm and the differential image coding algorithm are made. In the former case, video quality, compression ratio and encoding time is improved. In the latter case, video quality is degraded, but compression ratio and encoding time is improved. Consequently, the proposed method shows a reasonably good performance over existing ones.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.33-36
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• 2021

In this paper, we propose a new method for improving compression efficiency of immersive video using reinforcement learning. Immersive video means a video that a user can directly experience, such as 3DOF+ videos and Point Cloud videos. It has a vast amount of information due to their characteristics. Therefore, lots of compression methods for immersive video are being studied, and generally, a method, which projects an 3D image into 2D image, is used. However, in this process, a region where information does not exist is created, and it can decrease the compression efficiency. To solve this problem, we propose the reinforcement learning-based filling method with considering the characteristics of images. Experimental results show that the performance is better than the conventional padding method.

• Journal of the Korea Computer Graphics Society
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• v.6 no.3
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• pp.35-40
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• 2000

There are two problems in H.26X compression technique. One is compressing time in encoding process and the other is degradation of the decoded video quality due to high compression rate. For transferring moving pictures in real-time, it is required to adopt massively high compression. In this case, there are a lot of losses of an original video data and that results in degradation of quality. Especially degradation called by blocking artifact may be produced. The blocking artifact effect is produced by DCT-based coding techniques because they operate without considering correlation between pixels in block boundaries. So it represents discontinuity between adjacent blocks. This paper describes methods of quality compensation for H.26x decoded data and enhancing encoding speed for real-time operation. Our goal of the quality compensation is not to make the decoded video identical to a original video but to make it perceived better through human eyes. We suggest an algorithm that reduces block artifact and clears decoded video in decoder. To enhance encoding speed, we adopt new four-step search algorithm. As shown in the experimental result, the quality compensation provides better video quality because of reducing blocking artifact. And then new four-step search algorithm with $MMX^{TM}$ implementation improves encoding speed from 2.5 fps to 17 fps.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.102-111
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• 2020

An increase in high-quality video service continually leads to the standardization of high-performance video codecs such as the versatile video coding standard. Although such codecs have improved coding efficiency in terms of high fidelity, a tremendous increase in the amount of video data is required for more efficient compression, especially for efficiently recognizing and analyzing the target within the millions of objects/events captured every day, such as those by surveillance systems. Therefore, newly established MPEG standardization efforts have studied the new generation of video compression standards for machine vision-oriented video. This paper presents the standardization trends in video coding for machines and discusses further directions for improvement.