Associative Processors can perform parallel operations in massive scale because of which they are found to be efficient for video coding. Due to the inherent nature of the architecture, performing DCT becomes computationally intensive. To overcome this drawback, multiple DCTs are performed in parallel. This approach results in huge data traffic as it is performed for multiple blocks of video data. In this paper we present a new approach to perform DCT on associative processor. In this approach we make use of the shape of DCT basis vectors to extract parallelism. Such an approach reduces both the average number of cycles and the data traffic involved in video coding. Further, the video coding can be performed on Macro block basis thereby reducing a huge number of redundant operations.
Associative Processors have become popular because of their ability to perform parallel operations in massive scale. The use of Associative Processors especially for MPEG4/H.263 video coding was found to have low power consumption. However they lack the ability to perform computationally intensive block transforms. The paper
discusses requirements for video processing and shows how Associative Processors are more suited for video coding than RISC architectures. We highlight the various drawbacks of using Associative Processors for video coding and propose a new Distributed Arithmetic based enhancement to the architecture that provides greater flexibility in the implementation of video coding algorithms. These modifications help in faster computation of DCT and simulations of the proposed enhancement show that MPEG 4 simple profile encoder can be implemented in less than 10 MIPS.