Pipeline functional algorithms, data partitioning for adaptive transform coding algorithms

Image compression algorithms based on block transform coding have been adopted by the CCITT for coding of visual telephony. Recent developments in block transform coding show that by incorporating adaptive blocksizes, the efficiency in terms of bit-rate and subjective image quality of such coding methods are improved. The characteristics of this Scene Adaptive Transform Coding algorithm are introduced. The computational intensity and highly parallel nature of such algorithms motivate the use of a multiple processor network to execute the algorithms in close to real-time. The authors reveal a method to exploit a second degree of parallelism which aims to further increase the efficiency of the network. This second degree of concurrency is achieved by parallelising the functional algorithms. Pipelining methods are used to exploit the functional concurrency within the algorithms. By maintaining a suitable granularity of data partitioning and parallelising the functional algorithms, a balance in the computation to communication on multiprocessors can be achieved. The performance of the proposed Pipeline-Tree Architecture (PTA) is compared with the commonly used Tree structure