Spherical coding algorithm for wavelet image compression.

In recent literature, there exist many high-performance wavelet coders that use different spatially adaptive coding techniques in order to exploit the spatial energy compaction property of the wavelet transform. Two crucial issues in adaptive methods are the level of flexibility and the coding efficiency achieved while modeling different image regions and allocating bitrate within the wavelet subbands. In this paper, we introduce the "spherical coder," which provides a new adaptive framework for handling these issues in a simple and effective manner. The coder uses local energy as a direct measure to differentiate between parts of the wavelet subband and to decide how to allocate the available bitrate. As local energy becomes available at finer resolutions, i.e., in smaller size windows, the coder automatically updates its decisions about how to spend the bitrate. We use a hierarchical set of variables to specify and code the local energy up to the highest resolution, i.e., the energy of individual wavelet coefficients. The overall scheme is nonredundant, meaning that the subband information is conveyed using this equivalent set of variables without the need for any side parameters. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.

Synthesizing processed video by filtering temporal relationships.

Temporal relationships (motion fields) have been widely exploited by researchers for video processing. Their primary use has been to group pixels in spatiotemporal neighborhoods. Examples include coding and noise reduction. Typically, video processing is achieved by filtering, modeling, or analyzing pixels in these neighborhoods. In spite of the widespread use of motion information to process video, rarely are the fields treated as signals, i.e., the temporal relationships are seldom considered as a distinct time series. A notable exception is the generalized autoregressive modeling of these relationships in Rajagopalan et al. (1997). In this work, we present a generalization of finite impulse response filtering applicable to temporal relationships and continue the spirit of the work of treating motion fields as a distinct signal (albeit one that is closely tied to the pixel intensities). Applications presented are preprocessing of video for coding and for noise reduction. Instead of filtering pixels in spatiotemporal neighborhoods directly, we argue that it may be more beneficial to filter the temporal relationships first and then synthesize processed video. Simulations shows MPEG-1 rate gains of up to 20% for coding processed video compared to unprocessed ones where processing leaves the original perceptually unchanged. Noise reduction experiments demonstrate a gain of 0.5 dB at high signal to noise ratios over the best results in the published literature while at low to moderate SNRs, improvements are 0.3 dB lower.