• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.188-190
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• 2012

Compressed Sensing (CS) has been developed for several years. Among many CS algorithms for image, the Block-based Compressed Sensing with Smoothed Projected Landweber (BCS-SPL) demonstrates its excellent performance in low-complexity and near-optimal reconstruction. Several noise filtering algorithms of image reconstruction have been introduced such as the Wiener or the median filters, etc. In general, each filter has its own advantages and disadvantages depending on specific coding scheme. In this paper, we show that reconstruction performance can be varied according to the choice of filter. When a sub-rate value is changed, different filter causes different effect as well. Concerning the sub-rate, an inner filter can be chosen to improve the reconstructed image quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.133-134
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• 2018

MPEG-NNR (Compressed Representation of Neural Networks) aims to define a compressed and interoperable representation of trained neural networks. In this paper, a compressed representation of NN and its evaluation performance along with use cases of image/video compression in MPEG-NNR are presented. In the compression of NN, a CNN to replace the in-loop filter in VVC (Versatile Video Coding) intra coding is compressed by applying uniform quantization to reduce the trained weights, and the compressed CNN is evaluated in terms of compression ratio and coding efficiency compared to the original CNN. Evaluation results show that CNN could be compressed to about quarter with negligible coding loss by applying simple quantization to the trained weights.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.223-227
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• 1996

In this paper we propose an adaptive fast image restoration filter, which is suitable for reducing the blocking artifacts in the compressed image in real-time. The proposed restoration filter is based on the observation that quantization operation in a series of coding process is a nonlinear and many-to-one mapping operator. And then we propose an approximated version of constrained optimization technique as a restoration process for removing the nonlinear and space varying degradation operator. We also propose a novel block classification method for adaptively choosing the direction of a highpass filter, which serves as a constraint in the optimization process. The proposed classification method adopts the bias-corrected maximized likelihood, which is used to determine the number of regions in the image for the unsupervised segmentation. The proposed restoration filter can be realized either in the discrete Fourier transform domain or in the spatial domain in the form of a truncated finite impulse response (FIR) filter structure for real-time processing. In order to demonstrate the validity of the proposed restoration filter experimental results will be shown.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.2
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• pp.84-95
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• 2014

Recently, as a variety of techniques of CMF (Compressed media filter) that has advantages of high porosity and compressibility have been developed in the U.S. and Japan. Therefore, the interest of intensive wastewater treatment using CMF has grown. This study examined the feasibility of CMF with varying sewage water quality to determine the optimum operating conditions. A preliminary tracer test that investigated the filtering process under various compression and flow rate conditions was performed. In a high compression condition, different porosities were applied to each depth of the column. Therefore, a distinct difference between a theoretical value and results of tracer test was observed. For the TSS (Total suspended solid) removal and particle size distribution of CMF for pre-treatment water under the various compression conditions, the compression ratio of 30 percent as the optimal condition showed greater than 70% removal efficiency. In addition, the compression ratio of >15% was required to remove small-sized particles. Also, an additional process such as coagulation is necessary to increase the removal efficiency for < $10{\mu}m$ particles, since these small particles significantly influence the effluent concentration. Modeling results showed that as the compression rate was increased, TSS removal efficiency in accordance with each particle size in the initial filtration was noticeably observed. The modeling results according to the depth of column targeting $10{\mu}m$ particles having the largest percentage in particle size distribution showed that 150-300 mm in filter media layer was the most active with respect to the filtering.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.768-779
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• 2018

In this paper, we propose a novel video quality function for video processing based on MSSSIM to select an appropriate video processing filter and to accommodate multiple processing filters to each pixel block in a picture frame by a mathematical selection law so as to maintain video quality and to reduce the bitrate of compressed video. In viewpoint of video compression, since the properties of video quality and bitrate is different for each picture of video frames and for each areas in the same frame, it is difficult for the video filter with single property to satisfy the object of increasing video quality and decreasing bitrate. Consequently, to maintain the subjective video quality in spite of decreasing bitrate, we propose the methodology about the MSSSIM as the measure of subjective video quality, the KL-Divergence as the measure of bitrate, and the combination method of those two measurements. Moreover, using the proposed combinatorial measurement, when we use the multiple image filters with mutually different properties as a pre-processing filter for video, we can verify that it is possible to compress video with maintaining the video quality under decreasing the bitrate, as possible.

• 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.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.4
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• pp.629-637
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• 2000

A two-phase anaerobic reactor with a submerged microfiltration system was tested for its ability to produce methane energy from organic wastewater. A membrane separation system with periodic backwashing with compressed air was submerged in the acidogenic reactor. The cartridge type of microfiltration (MF) membrane with pore size of $0.5{\mu}m$ (mixed esters of cellulose) was tested. An AUBF (Anaerobic Upflow Sludge Bed Filter: 1/2 packed with plastic media) was used for the methanogenic reactor. Soluble starch was used as a substrate. The COD removal was investigated for various organic loading with synthetic wastewater of 5,000 mg starch/L. When the hydraulic retention time (HRT) of the acidogenic reactor was changed from 10 to 4.5 days, the organic loading rate (OLR) varied from 0.5 to $1.0kg\;COD/m^3-day$. When the HRT of the methanogenic reactor was changed from 2.8 to 0.5 days, the OLR varied from 0.8 to $5.8kg\;COD/m^3-day$. The acid conversion rate of the acidogenic reactor was over 80% in the 4~5 days of HRT. The overall COD removal efficiency of the methanogenic reactor showed over 95% (effluent COD was below 300 mg/L) under the highly fluctuating organic loading condition. A two-phase anaerobic reactor showed an excellent acid conversion rate from organic wastewater due to the higher biomass concentration than the conventional system. A methanogenic reactor combined with sludge bed and filter, showed an efficient COD and SS removal.