The MPEG-4 Fine Grained Scalability (FGS) profile aims at scalable layered video encoding, in order to ensure efficient video streaming in networks with fluctuating bandwidths. In this paper, we propose a novel technique, termed as FMOE-MR, which delivers significantly improved rate distortion performance compared to existing MPEG-4 Base Layer encoding techniques. The video frames are re-encoded at high resolution at semantically and visually important regions of the video (termed as Features, Motion and Objects) that are defined using a mask (FMO-Mask) and at low resolution in the remaining regions. The multiple-resolution re-rendering step is implemented such that further MPEG-4 compression leads to low bit rate Base Layer video encoding. The Features, Motion and Objects Encoded-MultiResolution (FMOE-MR) scheme is an integrated approach that requires only encoder-side modifications, and is transparent to the decoder. Further, since the FMOE-MR scheme incorporates "smart" video preprocessing, it requires no change in existing MPEG-4 codecs. As a result, it is straightforward to use the proposed FMOE-MR scheme with any existing MPEG codec, thus allowing great flexibility in implementation. In this paper, we have described, and implemented, unsupervised and semi-supervised algorithms to create the FMO-Mask from a given video sequence, using state-of-the-art computer vision algorithms.

With the availability of high-resolution cameras and increased computation power, it becomes possible to implement OCR applications such as business card reader in the mobile device. In this paper we introduced the design and implementation of a mixed-lingual business card reader based on build-in camera. It has the capability to recognize business cards with Chinese or English characters. In order to deal with the challenge of limited resource in mobile device, we proposed some new methods to reduce the resource requirement of the image processing and the Chinese OCR engine. Our experiment gives satisfactory result.