Although the mainstream of video coding technology continues to improve and iterate on previous generations, it seems clear that consumer demands on video content will continue to outstrip the savings made by better codecs. This is, in part, because mainstream codecs are rooted in a established paradigm that uses residual coding to maximise PSNR at a given bit rate. However, it is well known that PSNR as a metric for visual quality does not correlate well with viewers' subjective opinions. In recent years, research into residual-less approaches to video coding has become popular. The aim is to achieve t he best possible perceptual quality, irrespective of the PSNR with respect to the original. This allows the use of more advanced motion models, tuned to specific content within the video. This thesis proposes such an approach . Specifically, the motion of rigidly textured, planar regions is modelled using a perspective model, so that the decoder can interpolate these regions directly from reference frames. Prior knowledge of the scene is employed to condition the motion estimation process, in the form of keyframe models marked up under supervision. The motion estimation algorithm is able to compute planar motion parameters independently of the motion of foreground objects, and is so able to facilitate the detection of non-conforming regions. These algorithms are integrated with a host codec, which codes non-planar regions as normal. A subjective trial shows that this hybrid codec is able to achieve significant bit rate savings over the host codec, while maintaining quality.