Source coding of speech and video signals

Singhal, Sharad; Gall, Didier Le; Chen, C.-T.

doi:10.1109/5.56935

Cited by 14 publications

(1 citation statement)

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“…Although the optimal representative vectors in the transform domain are transforms of the optimal representative vectors in the time domain, and thus offer no added advantage, split vector quantization techniques can be employed more efficiently in the transform domain. Also, transform domain coding permits use of noise shaping techniques that may improve the subjective quality of the reproduced speech [6]. In split vector quantization, the transform coefficients are partitioned into subsets and vector quantization is used on each subset of the transform coefficients.…”

Section: Introductionmentioning

confidence: 99%

Energy-Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains

Mikhael

Krishnan

2001

Digital Signal Processing

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Vector quantization schemes are widely used for waveform coding of one-and multidimensional signals. In this contribution, a novel energybased, split vector quantization technique is presented, which represents digital signals efficiently as measured by the number of bits per sample for a predetermined signal reconstruction quality. In this approach, each signal vector is projected into multiple transform domains. In the learning mode, for a given transform domain representation, the transformed vector is split into subvectors (subbands) of equal average energy estimated from the transformed training vector ensemble. An equal number of bits is assigned to each subvector. A codebook is then designed for each equal energy subband of each transform domain representation. In the running mode, the coder selects codes from the domain that best represents the signal vector. The proposed multiple transform, split vector quantizer is developed and its performance is evaluated for both singlestage and multistage implementations. Several single transform vector quantizers for waveform coding exist, some of which employ energy-based bit allocation. Sample results using one-dimensional speech signals confirm the superior performance of the proposed scheme over existing single transform vector quantizers for waveform coding. 

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Section: Introductionmentioning

confidence: 99%