Earticle

본 논문에서는 모바일 영상을 위한 JPEG 양자화 테이블 설계 방법을 제안한다. 작은 화면에서 많은 정보를 표현해야 하는 모바일 영상의 특성을 고려하여 고주파 성분의 영향을 보정하도록 양자화 테이블을 모델링한다. 제안한 모델링 방법을 서비스되는 모바일 QVGA 영상에 적용하여 pre-emphasis factor를 최적화 하였다. 모바일 QVGA 영상에 대한 실험을 통하여 bpp와 PSNR이 향상됨을 보였다.

This paper presents a new JPEG quantization table design method for mobile images in handset. From the characteristics of the mobile images, we propose a modeling method of the quantization table and select the optimized pre-emphasis factor from various sets of 240⨯320 images. From the experiment, we show that the performance is improved in the sense of bpp and PSNR, applying the proposed method.

This paper presents JPEG quantization table design using RD optimization and neural network. Using R-D optimization, quantization table with good performance can be obtained. However, it is time-consuming and difficult to adopt to embedded systems. In this paper, a new quantization table design method is proposed using R-D optimization and neural network. Neural network learns the quantization table obtained from R-D optimization and produces a quantization table for the Images. The proposed system is applied to Yale face data. From the simulation results, it has been shown that the proposed codec has better performance than JPEG.

In this paper, a codec design method is pro-posed for the face images based on JPEG and its application to face recognition is presented. Quantization table design is dis-cussed using R-D optimization for Yale face data. For the usage in the embedded systems, fast codec design is also considered. The proposed codec has better performance than JPEG codec for face images. Through the recognition experiment using PCA and LDA, it has been shown that the proposed codec has better performance than JPEG codec.

The size of raw data has dramatically increased due to the recent trend of Synthetic Aperture Radar(SAR) development plans for high resolution and high definition image acquisition. The large raw data has an impact on satellite operability due to the limitations of storage and transmission capacity. To improve the SAR operability, the SAR raw data shall be compressed before transmission to the ground station. The Block Adaptive Quantization (BAQ) algorithm is one of the data compression algorithm and has been used for a long time in the spaceborne SAR system. In this paper, an optimization method of BAQ threshold table is introduced using real SAR raw data to prevent the degradation of signal quality caused by data compression. In this manner, a new variation estimation strategy and a new threshold method for block type decision are introduced.