Impact of rate control tools on very fast nonembedded wavelet image encoders Recently, there has been an increasing interest in the design of very fast wavelet image encoders focused on applications (interactive realtime imageu0026video applications, GIS systems, etc) and devices (digital cameras, mobile phones, PDAs, etc) where coding delay andor available computing resources (working memory and power processing) are critical for proper operation. Most of these fast wavelet image encoders are nonembedded in order to reduce complexity, so no rate control tools are available for scalable coding applications. In this work, we analyze the impact of simple rate control tools for these encoders in order to determine if the inclusion of rate control functionality is worth enough with respect to popular embedded encoders like SPIHT and JPEG2000. We perform the study by adding rate control to the nonembedded LTW encoder, showing that the increase in complexity still maintains LTW competitive with respect SPIHT and JPEG2000 in terms of RD performance, coding delay and memory consumption.

Embedded to lossless coding of motion compensated prediction residuals in lossless video coding Lossless video coding is useful in applications where no loss of information or visual quality is tolerable. In embedded to lossless coding an encoded video stream can be decoded into any bit rate up to the lossless bit rate, which is quite useful in numerous applications. In this paper, the research we present is based on lossless video coding, which uses motion compensated prediction to eliminate temporal redundancy. We specifically address the problem of embedded to lossless coding of the motion compensated prediction residuals. Since the statistical properties of the residuals are different from still images, the best wavelet bases for still images do not perform well for residuals. Since residuals contain a higher portion of high frequency information in high motion regions, a fixed transform for the entire frame is not very efficient. We introduce a spatially adaptive wavelet transform method, which takes the local frame statistics into account before choosing the wavelet base. This transform technique provided the best performance for most of the test frames.