Image processing group

In general, since data amount processed in image processing systems is quite large, system optimization considering both hardware and software is a key to success. In this research team, aiming at finding novel design method for image processing systems, we are carry on some researches about image processing systems focusing on video coded, video enhancement, and so forth.

Multiple video stream decoding scheme for mobile terminals


Recently we have had many opportunities to watch digital videos on mobile terminals, which include cellular phones and music players, owing to the One-Seg broadcasting service, video sharing websites, Internet television services, and so on. As the number of video contents distributed for mobile terminals has increased, the demand for video search interface to find a desired content quickly has grown. A system where multiple videos displayed on a mobile terminal can be a solution for this demand. On the other hand, some latest HDD and Blu-ray disc recorders can copy recoded videos to mobile terminals, and the display resolution of mobile terminals has become large. In such situation, the demand to watch high resolution videos on mobile terminals has increased. However, computer resources required for simultaneous decoding of multiple videos and decoding of high resolution videos are so considerable that such systems are difficult to realize. Motivated by this, we are researching about the performance optimization of video decoder to realize simultaneous decoding of multiple videos and decoding of high resolution videos on mobile terminals.

Motion-compensated frame interpolation based on feature tracking


Recent advance in the quality of LCDs makes it indispensable to enhance the quality of video sequences. There are two approached for video quality enhancement; one is to increase image spacial resolution, and the other is to increase image temporal resolution, which would be called frame rate. In this research, we are targeting frame rate enhancement by using motion compensated frame interpolation (MCFI). Since many existing MCFI methods are based on matching, it suffers from block noise. In contrast, we are focusing on MCFI based on feature tracking method which is used in object tracking field. The proposed method estimates motion vectors from tracked features, and interpolates the frame by pixel-by-pixel scheme. By using our method, high quality block-noise free video sequences can be generated.

A study on real-time Retinex video image enhancement


Recently, consumer digital imaging devices such as digital cameras and color liquid crystal displays have been gaining much popularity. Digital image enhancement in terms of color tone and/or contrast is indispensable for these kinds of consumer imaging devices. General image enhancement schemes, such as gamma correction and histogram equalization, are used for long years, which compensate each pixel value uniformly based on given equations or look-up tables. In contrast, adaptive image enhancement schemes may refer surrounding pixels to reproduce a high quality image. However, existing adaptive image enhancement schemes suffer from high computational cost, and therefore effective reduction of the computational cost is required for practical application. Motivated by this, in this research topic, we are developing an efficient real-time Retinex video image enhancement scheme, which includes hardware implementation. Currently, we focus on the Retinex theory and its quadratic programming (QP) model.

An image compression method for frame memory reduction


Recently, large sized plasma or LCD TV sets are gaining popularity, since high-definition video appeared such as digital terrestrial television and Blu-ray Disc. In these visual devices, a large frame memory is required, and large power is dissipated due to data transfer, because of increase of the data size. Motivated by this, we research about the method to reduce the size of the frame memory to install compresses and decompresses between an image processing part and a frame memory.

An efficient hardware architecture for adaptive deinterlacing


Interlaced video sequences are widely used for digital terrestrial television and camcorders. However, it cannot be be displayed accurately on LCDs, which display video sequences by progressive scanning. Therefore, it is necessary to convert video sequences from interlaced format to progressive format. This conversion is called deinterlacing, it is implemented as LSIs and processed at real-time in LCD TVs. Various deinterlacing methods has been researched, because it influences the quality of video so much. Methods based on motion compensation can achieve high-quality deinterlacing. However, they require much computational cost in terms of processing time and hardware resources. Motivated by this, we are researching about an efficient hardware architecture for deinterlacing to relief the computational cost, keeping its conversion quality high.

A study on noise evaluation scheme for consumer camcorders


Various kinds of noise are included in video sequences recorded by consumer camcorders. In addition, recent advance in camcorders in terms of its integration, such as sophisticated functionalities by signal processing with compact body, has made it impossible to separate each noise from the resulted video sequence. In order to evaluated noise during its development, video quality experts evaluate the video quality subjectively using prototype camcorders. This evaluation scheme requires much cost in terms of man-hours, and limits the turn around time of the development of camcorders. Motivated by this, we are researching about an efficient noise evaluation scheme and its implementation in terms of noise evaluation systems.

Last-modified: 2010-03-20 (Sat) 22:34:45