Hardware-Compatible Vertex Compression Using Quantization and Simplification

Budirijanto Purnomo, Jonathan Bilodeau Jonathan D. Cohen and Subodh Kumar,
ACM SIGGRAPH/Eurographics Symposium on Graphics Hardware 2005


Comparing our quantization result for NVIDIA RocketCar model to the standard API-supported quantizations. We can achieve a higher compression rate while maintaining higher image quality than other standard methods.

Abstract:

We present a vertex compression technique suitable for efficient decompression on graphics hardware. Given a user-specified number of bits per vertex, we automatically allocate bits to vertex attributes for quantization to maximize quality, guided by an image-space error metric. This allocation accounts for the contraints of graphics hardware by packing the quantized attributes into bins associated with the hardware's vectorized vertex data elements. We show that this general approach is also applicable if the user specifies a total desired model size. We present an algorithm that integrally combines vertex decimation and attribute quantization to produce the best quality model for a user-specified data size. Such models have an appropriate balance between the number of vertices and the number of bits per vertex. Vertex data is transmitted to and optionally stored in video memory in the compressed form. The vertices are decompressed on the fly using a vertex program at rendering time. Our algorithm not only work well within the constraints of current graphics hardware but also generalize to a setting where these constraints are relaxed. They apply to models with a wide variety of vertex attributes, providing new tools for optimizing space and bandwidth constraints of interactive graphics applications.

Some figures from the paper:

	A comparison of our quantization result at 64 bits per vertex versus the original.
	Comparing the image quality of the combined quantization and simplification versus quantization alone and simplification alone. (b)-(d) has the same storage size of 136KB.
	Computing the optimal bits per vertex of a given target size of 136KB for the bunny model.
	Performance using VBO for one or more copies of the Thai Statue model (original model versus compressed to 1/6 the size using quantization only) on a machine with 256MB video memory. Combining simplification makes the speedup more dramatic.

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