Parallel image compression using vector quantization

Summary form only given. The authors used a parallel approach to address the complexity issues of vector quantization: They implemented two full search memoryless parallel vector quantizers using 2 x 2 and 4 x 4 fixed block sizes on a shared memory MIMD machine, the BBN GP 1000. The squared error distortion measure and the LBG codebook design algorithm were used. The searching of the codebook is done in parallel for both the image coding and codebook design phases. The input vectors are in shared memory distributed among all the processor node memory modules. A private copy of the codebook is given to each processor node. A parallel task is generated for each input vector to be encoded. Each task is assigned one input vector to encode. The task searches the entire codebook to determine the minimum distortion codevector. The index of this vector is the output of the task. Load balancing of the tasks on the available processor nodes is done automatically by the operating system. This algorithm design requires minimal synchronization between the tasks to accumulate the total distortion. While good parallel performance was achieved the vector quantizers were generally lacking in fidelity. It is expected that these methods can be extended to achiwe high fidelity nhile maintaining good parallel performance.