Real-time vector quantization-based image compression on the SIMPil low memory SIMD architecture

Vector quantization (VQ) has become a popular technique for image compression. While conventional unstructured VQs have the potential of achieving the best theoretical performance, they are also demanding in storage and computational requirements. A significant amount of current research on VQ implementations addresses increasing the speed of image encoding, which is one of the most computationally expensive operations. This is typically accomplished by imposing structures, exploiting properties of the distance measure, or developing efficient and fast implementations. This paper proposes a parallel implementation of a full-search VQ encoding algorithm using a low memory, fine grain single instruction stream multiple data stream (SIMD) pixel processor (SIMPil) being developed at Georgia Tech. This implementation fully exposes the available parallelism of the encoding process and exploits the processing and I/O capabilities of the processor, resulting in a system that can perform real-time image and video compression. The proposed implementation encodes a large region of the original image at once, replacing each constituent input block with its corresponding VQ codeword index. Preliminary simulation results indicate that the proposed implementation is capable of sustain real-time frame rates. A prototype single node SIMPil implementation has been fabricated by MOSIS in 0.8 μm CMOS, and is being evaluated