Video transcoding is one of the key technologies in modern multimedia systems to enable effective access for a wide diversity of devices and formats. In applica-tions that deliver live videos over bandwidth limited channel to multiple users, a transcoder is needed to provide dynamic adaptations in the bit-rate and the format of the video bit-stream in order to serve different users simultaneously. However, a robust and flexible transcoder usually requires a cascaded decoding and re-encoding, which is computational expensive. Thus, it is important to re-use the information of the input video bit-stream as efficient as possible to reduce the complexity while maintaining high output quality....[ Read more ]

Video transcoding is one of the key technologies in modern multimedia systems to enable effective access for a wide diversity of devices and formats. In applica-tions that deliver live videos over bandwidth limited channel to multiple users, a transcoder is needed to provide dynamic adaptations in the bit-rate and the format of the video bit-stream in order to serve different users simultaneously. However, a robust and flexible transcoder usually requires a cascaded decoding and re-encoding, which is computational expensive. Thus, it is important to re-use the information of the input video bit-stream as efficient as possible to reduce the complexity while maintaining high output quality.

In addition, the transcoding process can be further speed up by making use of computer graphics hardware. Graphics processing units (GPUs) provide tremen-dous memory bandwidth and computational horsepower, and general-purpose computation on GPUs (GPGPU) has demonstrated a great success in recent years. Some researchers have used GPU for image and video processing, such as transformation, motion compensation and motion estimation, etc. Further re-search on using GPU for video processing, in particular video encoding, is needed to exploit the possibilities of speed improvement.

This thesis is divided into two parts. We first address the algorithm-level so-lutions for fast transcoding by re-using the information extracted from the input video bit-stream. Two rate control schemes are proposed. The first proposed method, namely Zero-Residue Pre-Selection (ZRPS), reduces the computational complexity of a well-known rate control scheme TMN-8 by selecting a sub-set of macroblocks for the calculations. The rate information of the input video bit-stream is used to select these macroblocks. Another rate control scheme, Low-Complexity Rate Control (LCRC), is proposed to use the relationship between the rate of the input video bit-stream and the output video bit-stream on a per-row basis. Since no explicit complexity measurement of macroblocks is needed, it is good for H.264/AVC under the rate-distortion optimization framework. In addition, we also propose a motion vector refinement scheme - Minimum Cost Tendency Search (MCTS) for transcoding from H.263 to H.264/AVC. MCTS ex-plicitly considers the difference between their cost functions for motion estimation and results in a more stable performance over a wide range of output quality. In the second part of this thesis, a GPU-based motion estimation for H.264/AVC is presented. It is flexible enough that we can apply it on a wide variety of graphics chips and obtain a good performance with simple fine tuning.