Efficient VQ-Based Normal Map Compression Using Quality Estimation Model

Abstract

Normal maps play an important role in realistic 3D image rendering.They are used to express pseudo roughness of the 3D objects' surface with a small amount of polygon data.In this paper,a fast and efficient normal map compression algorithm using vector quantization is developed based on a quality estimation model of the rendered images.Optimal encoding strategy is mathematically obtained from the quality estimation model.Experimental results demonstrated that the algorithm developed in this paper gives better result than other previous approaches especially in higher PSNR region.As a result,it has been made possible to encode normal maps considering the quality of the resultant rendered images without actually rendering the images.Encoding time is made reasonable by considering the limited signal space of normal maps and employing inner product in the nearest vector search. In addition,decoding time is also very small,which is also an important factor for practical usage.