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Data Compression Conference (DCC '95)
Hierarchical vector quantization of perceptually weighted block transforms
Snowbird, Utah
March 28-March 30
ISBN: 0-8186-7012-6
Navin Chaddha, Comput. Syst. Lab., Stanford Univ., CA, USA
Mohan Vishwanath, Comput. Syst. Lab., Stanford Univ., CA, USA
P.A. Chou, Comput. Syst. Lab., Stanford Univ., CA, USA
This paper presents techniques for the design of generic block transform based vector quantizer encoders implemented by table lookups. In these table lookup encoders, input vectors to the encoders are used directly as addresses in code tables to choose the codewords. There is no need to perform the forward or reverse transforms. They are implemented in the tables. In order to preserve manageable table sizes for large dimension VQ's, we use hierarchical structures to quantize the vector successively in stages. Since both the encoder and decoder are implemented by table lookups, there are no arithmetic computations required in the final system implementation. The algorithms are a novel combination of any generic block transform (DCT, Haar, WHT) and hierarchical vector quantization. They use perceptual weighting and subjective distortion measures in the design of VQ's. They are unique in that both the encoder and the decoder are implemented with only table lookups and are amenable to efficient software and hardware solutions.
Index Terms:
vector quantisation; table lookup; transform coding; perceptually weighted block transforms; hierarchical vector quantization; generic block transform; vector quantizer encoders; codewords; table lookup encoders; table sizes; decoder; encoder; DCT; Haar transforms; WHT; perceptual weighting; subjective distortion measures; hardware solutions; software solutions
Citation:
Navin Chaddha, Mohan Vishwanath, P.A. Chou, "Hierarchical vector quantization of perceptually weighted block transforms," dcc, pp.3, Data Compression Conference (DCC '95), 1995
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