낮은 피사계 심도 JPEG2000 이미지를 위한 자동 관심영역 추출기반의 개선된 동적 관심영역 코딩 방법 A Revised Dynamic ROI Coding Method Based On The Automatic ROI Extraction For Low Depth-of-Field JPEG2000 Images원문보기
본 논문에서는 낮은 피사계 심도 JPEG2000 이미지의 복원 과정에서 관심영역을 자동으로 추출하여 우선적 처리하는 개선된 동적 관심영역 코딩 방법을 제안한다. 제안한 방법은 기존 방법과는 달리 사용자의 관심영역 지정 과정을 거치지 않고, DWT(Discrete Wavelet Transform)에서 특정 레벨의 고주파 서버 밴드를 사용하여 에지 마스크 정보를 생성한 후에 자동 에지 코드 블록 판별 알고리즘을 사용하여 관심영역을 빠르게 처리한다. 이 알고리즘은 에지 임계값과 4 방향(동, 서, 남, 북)으로 코드 블록 단위의 에지 마스크 정보를 이용하여 에지 코드 블록을 판별한다. 본 알고리즘을 기존의 Implicit 방법에 적용하여 실험한 결과, 제안한 방법이 기존의 방법들에 비해 속도와 품질 면에 있어서 우수함을 확인하였다.
본 논문에서는 낮은 피사계 심도 JPEG2000 이미지의 복원 과정에서 관심영역을 자동으로 추출하여 우선적 처리하는 개선된 동적 관심영역 코딩 방법을 제안한다. 제안한 방법은 기존 방법과는 달리 사용자의 관심영역 지정 과정을 거치지 않고, DWT(Discrete Wavelet Transform)에서 특정 레벨의 고주파 서버 밴드를 사용하여 에지 마스크 정보를 생성한 후에 자동 에지 코드 블록 판별 알고리즘을 사용하여 관심영역을 빠르게 처리한다. 이 알고리즘은 에지 임계값과 4 방향(동, 서, 남, 북)으로 코드 블록 단위의 에지 마스크 정보를 이용하여 에지 코드 블록을 판별한다. 본 알고리즘을 기존의 Implicit 방법에 적용하여 실험한 결과, 제안한 방법이 기존의 방법들에 비해 속도와 품질 면에 있어서 우수함을 확인하였다.
In this study, we propose a revised dynamic ROI (Region-of-Interest) coding method in which the focused ROI is automatically extracted without help from users during the recovery process of low DOF (Depth-of-Field) JPEG2000 image. The proposed method creates edge mask information using high frequenc...
In this study, we propose a revised dynamic ROI (Region-of-Interest) coding method in which the focused ROI is automatically extracted without help from users during the recovery process of low DOF (Depth-of-Field) JPEG2000 image. The proposed method creates edge mask information using high frequency sub-band data on a specific level in DWT (Discrete Wavelet Transform), and then identifies the edge code block for a high-speed ROI extraction. The algorithm scans the edge mask data in four directions by the unit of code block and identifies the edge code block simply and fastly using a edge threshold. As the results of experimentation applying for Implicit method, the proposed method showed the superiority in the side of speed and quality comparing to the existing methods.
In this study, we propose a revised dynamic ROI (Region-of-Interest) coding method in which the focused ROI is automatically extracted without help from users during the recovery process of low DOF (Depth-of-Field) JPEG2000 image. The proposed method creates edge mask information using high frequency sub-band data on a specific level in DWT (Discrete Wavelet Transform), and then identifies the edge code block for a high-speed ROI extraction. The algorithm scans the edge mask data in four directions by the unit of code block and identifies the edge code block simply and fastly using a edge threshold. As the results of experimentation applying for Implicit method, the proposed method showed the superiority in the side of speed and quality comparing to the existing methods.
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문제 정의
This image has strengths in bringing out a selected area and in tak and p only a small space, and th only a n tak and p onimage space, such as portraits, including wn ding or product photos. This study explains the dynamic ROI coding method for a low DOF JPEG2000 image. In order to encode ROI, an ROI extraction process must be applied.
Therefore rather than transferring and recovering all the image data, there have been studies on methods that process selected specific regions or a user's region of interest first. This study will mainly focus on the ROI coding method of JPEG2000. The ROI coding method not only enables viewing high-quality ROI at a low bit-rate, but also provides an excellent trade-off between quality and compression (1-3L This coding method is divided into a static coding method, in which ROI definition and coding are carried out during the compression process of turning the original image into a JPEG2000 image, and a dynamic method in which ROI definition and coding are performed during the recovery process of a JPEG2000 image that was not ROI-coded.
가설 설정
In images c), we can only see the ROI part in high quality since the Maxshift method codes the backgrounds after processing all of the ROI. In d), we can see the ROI in high quality, whereas the background shows poor quality. Finally, images d) look superior to images a) and b) in subjective evaluation of the whole image at low bit rates.
제안 방법
In our experiments we tested a variety of methods including: the EBCOT method where any ROI are not coded, the Maxshift method, and the proposed method. The first two methods were evaluated by using Kakadu software V3.
In this study, the DWT characteristics of JPEG2000 were used to propose a revised dynamic ROI coding method based on automatic ROI extracthn. The proposed method uses edge critical value and 양dge code block critical value for noise reduction and the edge distinction process; and for a faster ROI extraction, a four-way scan was done to search the edge code block that is first contacted for processing.
So it is possible to handle the precise ROI, real time execution can not be possible caused by the increasing time complexity. On the contrary, the proposed method is possible to process fastly, since it identifies and codes independently ROI code blocks by the edge threshold using the edge information by the unit of code block.
The first two methods were evaluated by using Kakadu software V3.0.7〔17〕, and the proposed method was evaluated by modifying the Kakadu software. For fair experimental conditions, we set WR0I to 4096.
The processing stages of the proposed method are based on the edge mask data to eliminate noise and extract an automatic ROI, then uses the ROI information to do preferred processing and u也mat시y reconstruct the ROI image. For efficient processing, if the size of ROI takes up more than 90% of the overall image, the image is reconstructed without ROI coding: this is due to the overhead on ROI coding.
The proposed method uses edge critical value and 양dge code block critical value for noise reduction and the edge distinction process; and for a faster ROI extraction, a four-way scan was done to search the edge code block that is first contacted for processing. Various experiments showed that the proposed method functioned the best under a low bit-rate with code block size 32x32.
The filtered outputs are then down sampled by a factor of 2 again. The transform results in four new sub-bands at each level of decoriposition； namely, an approximation sub-band at low resolution, LL, and three directionally sensitive detail sub-bands: HLrvertical features (horizontally hi或！ pass), IHtaizoHtal image features(vertically hiei pass), and HH- diagonal features (horizontally and vertically hi度! pass). Twcrlevel decoiwosition, seen in Figure 3, is processed to the second decomposition, only using the LL sub- as input
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박순화, 서영건, 이부권, 강기준, 김호용, 김형준, 김상복, "피사계 심도가 낮은 이미지에서 웨이블릿 기반의 자동 ROI 추출 및 마스크 생성." 한국 컴퓨터정보학회논문지, 제14권, 제 3호, 93-101쪽, 2009년 3월.
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