CS 395 T Real-Time Graphics Architectures,

Algorithms, and Programming Systems Spring’03

Vector Quantization

for Texture Compression

Qiu Wu

Dept. of ECE & TICAM

University of Texas at Austin

Feb. 4th, 2003

Overview

Review of Vector QuantizationVQ for texture compression

(paper of Beers etc) discussion of its contributions

and weaknessDXT1 Texture Compression

Signal Compression

Purpose : reduce the bit rate for

transmission/storage

Principles:Exploit the signal redundancy in spatial

domainHuman vision system has different

sensitivities for different frequencies, low frequency noise is more noticeable

Signal Compression

1. Reconstruction error==0? Lossy Compression: often eliminate high

frequency components Lossless Compression: exact reconstruction

2. Scalar based or Vector based Quantization?

Scalar Quantization : transform+SQ, state of the art

Vector Quantization

Vector Quantization

Block coding: consecutive n pixels as a block(vector)

Map each vector to an index of the code which is closest to this vector

This index is compressed info In decoding, look up table for a

vector by this index

Training algorithm: GLA

1. Begin with an initial codebook C1. 2. Repeat

(a) Given a codebook (set of clusters defined by their centroids) redistribute each vector x into one of the clusters in by selecting the one whose centroid is closer to x.

(b) Recompute the centroids for each cluster just created, to obtain the new codebook Cm+1.

(c) Compute the average distortion Dm+1

for Cm+1, Until the distortion has only changed by a

small enough amount since last iteration.

Animation of LBG Algorithm(http://www.data-compression.com/in

dex.html)

Pros and Cons of VQ

Pros: Fast decoding ---lookup table

Cons: Long training time, unstable process, low CR,

VQ for texture compression

Presented by AndrewC.Beers,

ManeeshAgrawala,and NavinChaddha on

SIGGRAPH 1996 VQ for generating compressed

texture in memory VQ for compressed MIPMAP Performance: CR=20, 2—20%

saving on time

VQ scheme of Beer’s paper

Details of VQ for texture compression

Multiple-codebooks or Single codebook

Option 1: Each codebook for each color component

Option 2: Components of color as a single value

Too simple description and just experimental, more statistical discussion would be necessary and wonderful

Details of VQ for texture compression

Large codebook or Small codebook

Large codebook---more representative vectors---better reconstruction quality

but More bits per index—>low compression ratio

Vice versa for small codebook

Details of VQ for texture compression

RGB-YUV

• More CPU time for tansforming back and forth though additional compression

• Just mention borrowing concept from video coding, no reason given in the paper, then in coding YUV combined together or in separate?

My explanation: more energy in Y component

Details of VQ for texture compression

(Fig 2 of Beers’ paper)

Mipmaping

DXT1 texture compression

compressed textures stored in memory at a ratio of 4:1

supported by Direct3D 4 by 4 pixel blocks---compressed

block has a size of 64 bits while an original, uncompressed block is 4x4x16 = 256 bits in size

DXT1 texture compression:Opaque Case

When compressing an entirely opaque block, two extreme colours are chosen from all the colours present in the original block

All 16 pixels contained in a block are represented with two bits each. These two bits enable four possible combinations to encode a pixel color

While two of these combinations indicate to use either of the stored colours, the other two specify a gradient of the defined colours

Reference:

Image coding using vector quantization: A review. IEEE Transactions on Communications, 36(8):957--971, August 1988.

Rendering from Compressed Textures",Beers, Agrawala and Chaddha, SIGGRAPH 1996.

DXT1 Texture Compression", Imagination Technologies