Reduced-complexity iterative post-filtering of video
There are numerous methods of post-processing that make use of iterative techniques. Many of these schemes have been demonstrated to be very effective in removing artifacts from compressed video, producing better and better image estimates at each iteration. However, this artifact removal comes at the cost of a large computational burden. This paper introduces two methods for iterative post-processing of compressed video in an efficient manner. One of these methods is applicable to one particular maximum a posteriori scheme. The other method has direct application to other, more general, iterative post-processing schemes that make use of a convex constraint set, which is the set of all images that will recompress to yield the originally-received data. The combination of these two methods produces a post-processing algorithm that has the advantage of many iterative schemes (excellent visual results), while requiring a relatively low amount of computational effort to achieve these results.