• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 1부
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• pp.982-987
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• 2007

The necessity of data compression scheme for volume data has been increased because of the increase of data capacity and the amount of network uses. Now we have various kinds of compression schemes, and we can choose one of them depending on the data types, application fields, the preferences, etc. However, the capacity of data which is produced by application scientists has been excessively increased, and the format of most scientific data is 3D volume. For 2D image or 3D moving pictures, many kinds of standards are established and widely used, but for 3D volume data, specially time-varying volume data, it is very difficult to find any applicable compression schemes. In this paper, we present a compression scheme for encoding time-varying volume data. This scheme is aimed to encoding time-varying volume data for visualization. This scheme uses MPEG's I- and P-frame concept for raising compression ratio. Also, it transforms volume data using Daubechies D4 filter before encoding, so that the image quality is better than other wavelet-based compression schemes. This encoding scheme encodes time-varying volume data composed of single precision floating-point data. In addition, this scheme provides the random reconstruction accessibility for an unit, and can be used for compressing large time-varying volume data using correlation between frames while preserving image qualities.

• 한국통신학회논문지
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• 제28권2C호
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• pp.100-109
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• 2003

가변길이 나무구조 벡터양자화기(VLTSVQ : variable-length tree-structured vector quantizer)를 기반으로 하는 영상 부호화 방식인 차분 인덱스(DI : Differential index) 할당기법을 제안하였다. 각 소스벡터는 VLTSVQ의 단말 노드로 양자화가 되어지고, 각 단말 노드는 유일한 이진 벡터로 표현된다. 제안한 방법은 영상의 화질은 유지하면서 압축률을 개선하기 위해 이웃하는 영상 블록들간의 상관성을 이용하였다. 모의실험을 통하여 제안한 방법이 기존 방법들에 비해 매우 높은 압축률을 보였으며, 화소간의 상관성이 높은 영상에 대해서는 63.2%의 발생 비트율 감소를 확인하였다. 그리고 영상내의 이웃블록들간 상관성이 커질수록 더 큰 비트율이 감소됨을 보였다. 제안한 부호화 기법은 일반적으로 이웃된 화소들간에 높은 상관성을 가진 MR(magnetic resonance)영상 부호화에 효율적으로 사용될 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1685-1688
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• 2003

The modem multimedia applications which are video Processor, video conference or video phone and so forth require real time processing. Because of a large amount of image data, those require high compression performance. In this paper, the proposed image processing encoder was designed by using wavelet transform encoding. The proposed filter block can process image data on tile high speed because of composing individual function blocks by parallel and compute both highpass and lowpass coefficient in the same clock cycle. When image data is decomposed into multiresolution, the proposed scheme needs external memory and controller to save intermediate results and it can operate within 33㎒.

• 한국게임학회 논문지
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• 제13권6호
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• pp.35-42
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• 2013

본 논문에서는 모바일 기기의 프로세서 처리 능력과 메모리 용량의 제한에 관계없이 거대 크기의 이미지를 게임의 배경으로 사용하기 위한 타일화된 이미지 압축 기법을 제안한다. 실험을 통하여 제안한 기법이 압축 효율 측면에서는 하나의 PNG 파일로 압축하는 방법과 유사하나 모바일 단말기의 메모리 제한으로 인한 디코딩 가능한 이미지의 크기 제한 문제를 해결할 수 있음을 보였다. 또한 실행 시 부분 디코딩 기법을 적용하여 단말기 화면에 출력되는 이미지 부분만 디코딩하여 출력함으로 초기의 대규모 이미지의 로딩 시간을 줄일 수 있음을 보였다.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1665-1670
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• 2004

The general fractal image compression provides a high compression rate, but it requires a large encoding time. In order to overcome this disadvantage, many researchers have introduced various methods that reduce the total number of domain blocks considering their block similarities or control the number of searching domain block depending on its distribution. In this paper, we propose a method that can reduce the number of searching domain blocks employing the variance coherence of intensity values and also the number of range blocks requiring the domain block search through the classification of range blocks. This proposed method effectively reduces the encoding time and also a negligible drop of the quality as compared with the previous methods requiring the search of all range blocks.

• 한국컴퓨터그래픽스학회논문지
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• 제30권3호
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• pp.81-86
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• 2024

영상 신호 프로세서(Image Signal Processor, ISP)는 카메라 센서로부터 획득된 RAW 영상을 사람의 눈에 보기 좋은 sRGB 영상으로 변환한다. RAW 영상은 sRGB 영상에 비해 영상 처리에 도움이 되는 정보를 가지고 있지만 상대적으로 큰 용량으로 인해 주로 sRGB 영상만 저장되고 사용된다. 또한, 실제 카메라의 ISP 과정이 공개되어 있지 않아 그 역과정을 모사하는 것은 매우 어렵다. 이에 sRGB와 RAW 영상의 상호 변환을 위한 카메라 ISP 모델링 연구가 활발히 진행되고 있으며, 최근 기존의 단순한 ISP 신경망 구조를 고도화하고 실제 카메라 ISP의 동작과 유사하게 카메라 파라미터(노출 시간, 감도, 조리개 크기, 초점 거리)를 직접 반영하는 ParamISP[1] 모델이 제안되었다. 하지만 ParamISP[1]를 포함한 기존의 연구는 카메라 ISP를 모델링함에 있어 렌즈로 인해 발생하는 렌즈 쉐이딩(Lens Shading), 광학 수차(Optical Aberration), 렌즈 왜곡(Lens Distortion) 등을 고려하지 않아 복원 성능에 한계가 있다. 본 연구는 ISP 신경망이 렌즈로 인해 발생하는 열화를 보다 잘 다룰 수 있도록 위치 정보 인코딩(Positional Encoding)을 도입한다. 제안하는 위치 정보 인코딩 기법은 영상을 분할하여 패치(Patch) 단위로 학습하는 카메라 ISP 신경망에 적합하며 기존 모델에 비해 영상의 공간적 맥락을 반영할 수 있어 더욱 정교한 영상 복원을 가능하게 한다.

• 전자공학회논문지S
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• 제34S권1호
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• pp.124-131
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• 1997

There have been many demands for th ereal-time image compression. The image compression systems have a wide range of applications. However, real-time encoding is hard to implement because it needs a large amount of computations. In particular, the discrete cosine transform (DCT) and motion estimatio require a large number of arithmetic oeprations compared to other algorithms in MPEG-2. The conventional fasdt DCT algorithms have focused on the reduction of the number of additions more cycles and more expense in realization. Because TMS320C80 has special structure, new approach for implementation of DCT is suggested. The selection of adaptive algorithm and optimization is requried TMS320C80 are analyzed an dsome adaptive DCT algorithms are selected. The DCT algorithms are optimized and implemented. Chens and lees DCT algorithms among various fast algorithms are selected because 1-D approach is effective in the view of th einternal structure of TMS320C80. According to the simulation result, Lees algorithm is more effective in speed and has little difference in precision. On the basis of the result, the possibility of DCT implementation for real-time MPEG-2 system is verified and the required number of the processor, called advanced DSP, is decided for real-time MPEG-2 encoding and decoding.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.358-360
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• 2004

The digital images contain a significant amount of redundancy and require a large amount of data for their storage and transmission. Therefore, the image compression is necessary to treat digital images efficiently. The goal of image compression is to reduce the number of bits required for their representation. The image compression can reduce the size of image data using contractive mapping of original image. Among the compression methods, the mapping is affine transformation to find the block(called range block) which is the most similar to the original image. In this paper, we applied the neural network(SOM) in encoding. In order to improve the performance of image compression, we intend to reduce the similarities and unnecesaries comparing with the originals in the codebook. In standard image coding, the affine transform is performed with eight isometries that used to approximate domain blocks to range blocks.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.706-709
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• 2002

This paper describes an effective technique for coding video sequences based on JPEG2000 codec. In the proposed method, multiple frames are combined into one large picture by interleaving each pixel data. A large picture enables images to be coded more efficiently and image quality is improved. A video sequence is efficiently coded by adapting the time correlation of the video sequences to spatial correlation. We demonstrated the effectiveness of this method by encoding video sequences using JPEG2000.

• Journal of Computing Science and Engineering
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• 제11권1호
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.