• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1650-1657
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• 2007

In this paper, we propose an Overlapped Block and an Hanning Window to prevent a Block Discontinuity when we use an algorithm to eliminate ringing artifact which is based on a block structure. The algorithm to eliminate ringing artifact operates with a block structure and 24-RGB data and is based on a modified K-means algorithm. The proposed overlapped block method is piled up one on another per an half of the size of unit-block when an input image is split into several unit-blocks. Therefore, we define a data unit as the unit-block of the block size, $16{\times}16$ pixels. We reconstruct the processed data units into the original form of input image by using an isotropic form of Hanning Window. Finally, in order to evaluate the performance of the abovementioned algorithms, we compare three image, an input image with ringing artifact, an image result obtained by conventional method (non-overlapped), and an image result obtained the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.576-582
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• 2011

In this paper, we proposed the improved Advanced Detail Enhancement algorithm that improve the blurring by the lossy compression with CODEC and reduce the ringing artifacts in restoration. The conventional algorithm needs much amount of the process by the use of RGB color space. To improve this, we only used the luminance value in YCbCr color space. We verified that the performance of the improved algorithm with Y color value, the luminance value, is equal to the conventional algorithm with RGB color value and that the operation time of the improved is shorter about 24% than the conventional through the measurement of the operation time with Kodak standard images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.272-273
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• 2019

VVC(Versatile Video Coding)는 YUV 입력 영상에 대하여 Luma 성분과 Chroma 성분에 대하여 각각 다른 최적의 방법으로 블록분할 후 해당 블록에 대해서 화면 내 예측 또는 화면 간 예측을 수행하고, 예측영상과 원본영상의 차이를 변환, 양자화하여 압축한다. 이 과정에서 복원영상에는 블록화 노이즈, 링잉 노이즈, 블러링 노이즈 발생한다. 본 논문에서는 인코더에서 원본영상과 복원영상의 잔차신호에 대한 MAE(Mean Absolute Error)를 추가정보로 전송하여 이 추가정보와 복원영상을 이용하여 Deep Learning 기반의 신경망 네트워크로 영상의 품질을 높이는 방법을 제안한다. 복원영상의 노이즈를 감소시키기 위하여 영상을 $32{\times}32$블록의 임의로 분할하고, DenseNet기반의 UNet 구조로 네트워크를 구성하였다.