It is currently a very exciting and challenging time for video compression. The predicted growth in demand for bandwidth, especially for mobile services is driven largely by video applications and is probably greater now than it has ever been. There are four reasons for this: (i) Recently introduced formats such as 3D and multiview, coupled with increasing dynamic range, spatial resolution and framerate, all require increased bit-rate to deliver improved immersion; (ii) Video-based web traffic continues to grow and dominate the internet; (iii) User expectations coninue to drive flexibility and quality, with a move from linear to non-linear delivery; (iv) Finally the emergence of new services, in particular mobile delivery through 4G/LTE to smart phones. While advances in network and physical layer technologies will no doubt contribute to the solution, the role of video compression is also of key importance. This research project is underpinned by the assumption that, in most cases, the target of video compression is to provide good subjective quality rather than to minimise the error between the original and coded pictures. It is thus possible to conceive of a compression scheme where an analysis/synthesis framework replaces the conventional energy minimisation approach. Such a scheme could offer substantially lower bitrates through reduced residual and motion vector coding. The approach proposed will model scene content using combinations of waveform coding and texture replacement, using computer graphic models to replace target textures at the decoder. These not only offer the potential for dramatic improvements in performance, but they also provide an inherent content-related parameterisation which will be of use in classification and detection tasks as well as facilitating integration with CGI. This has the potential to create a new content-driven framework for video compression. In this context our aim is to shift the video coding paradigm from rate-distortion optimisation to rate-quality modelling, where region-based parameters are combined with perceptual quality metrics to inform and drive the coding and synthesis processes. However it is clear that a huge amount of research needs to be done in order to fully exploit the method's potential and to yield stable and efficient solutions. For example, mean square error is no longer a valid objective function or measure of quality, and new embedded perceptually driven quality metrics are essential. The choice of texture analysis and synthesis models are also important, as is the exploitation of long-term picture dependencies.