Digital video compression is the enabling technology in many multi-media applications. These compression algorithms reduce the bit-rate requirements for transmitting digital video and reduce delivery costs. With these appealing properties, digital video is rapidly becoming an experience of everyday life. For example, video telephony assists corporate and research users in a variety of collaborations utilizing the Public Switched Telephone Networks or the Internet. DVD players, High-Definition Television devices, digital camcorders, digital VCRs and time-shifting products provide consumers with enhanced entertainment environments and novel methods for accessing media content. In the near future, wireless videophones promise un-tethered video communication between users.Several compression standards are key to the success of digital video applications. These standards are targeted at different viewing and transmission environments and include ITU's H.261, H.263 and H.263+ as well as MPEG's MPEG-1, MPEG-2, MPEG-4 and MPEG-4v2. Most service providers and manufacturers prefer to develop and manufacture products that are compliant with one of these standards. However, it is important to realize that these standards are not bit exact, in that they only specify the decoder. Each manufacturer designs the encoder, rate-control algorithms and pre- and post-processing filters. These are critical components that distinguish standard compliant products. As such, they are of significant interest to signal processing researchers and practicing engineers.In this paper and presentation, we will provide a tutorial introduction to pre- and post-processing algorithms for digital video compression. The goal of a post-processing algorithm is to reduce the visible and objectionable degradations that appear in the reconstructed video. These degradations appear due to the independent block-by-block processing and quantization methods that are at the core of modern compression systems. Work within this area ranges from very simple, image averaging algorithms to highly adaptive, image recovery techniques. These recovery techniques include the Constrained Least Squares and Projection onto Convex Sets methodologies, which we will discuss in the paper and presentation. Temporal filters are also an important component of many post-processing algorithms and will be included in the discussion.