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 FMOEMR,
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-Multi-
Resolution (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.
Body Animation Parameters (BAPs) are used to animate MPEG-4 compliant virtual human-like characters. In order to stream BAPs in real time interactive environments, the BAPs are compressed for low bitrate representation using a standard MPEG-4 compression pipeline. However, the standard MPEG-4 compression is inefficient for streaming to power-constrained devices, since the streamed data requires extra power in terms of CPU cycles for decompression. In this paper, we have proposed and implemented an indexing technique for a BAP data stream, resulting in a compressed representation of the motion data. The resulting compressed representation of the BAPs is <sup>1</sup>superior to the MPEG-4-based BAP compression in terms of both, required network throughput and power consumption at the client end to receive the compressed data stream and extract the original BAP data from the compressed representation. Although the resulting motion after de-compression at the client end is lossy, the motion distortion is minimized by intelligent use of the hierarchical structure of the skeletal avatar model. Consequently, the proposed indexing method is ideal for streaming of motion data to power- and network-constrained devices such as PDAs, Pocket PCs and Laptop PCs operating in battery mode and other devices in a mobile network environment.