Fast and Efficient Normal MAP Compression Based on Vector Quantization

Normal maps play an important role in realistic 3D image rendering to express pseudo roughness of the surface with small amount of polygon data. In this paper, a fast and efficient normal map compression algorithm is proposed based on vector quantization and entropy coding. Using the strong correlation among x, y, and z components of normal maps owing to the unity condition, compression ratio has been made much better than conventional approaches. In addition, the encoding time has been made reasonable by considering the distribution of the data and employing inner product in nearest-neighbor search instead of Euclidian distance taking advantage of the unity condition of the training data