Publication

Locally Temporal Adaptive Transform Scheme for Sub-band Video Coding

Pierre Vandergheynst, Oscar Divorra Escoda
2003
Conference Papers

Abstract

The work presented in this paper extends the concept of sub-band video coding based on a 3D wavelet transform to a more adaptive approach. A formal comparison is presented between the performances inferred by the use of the 3D wavelet transform and the use of a 2D wavelet in the spatial domain extended by a locally adaptive transform in the temporal dimension. Some advantages are foreseen for the new scheme since it is able to better deal with certain signal models like appearing and moving edges. An increased control of the distortion spreading is expected and consequently a lower visual impact relevance.

Official source

https://infoscience.epfl.ch/entities/publication/efef086a-a038-4b40-b310-26f8de272188

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Pierre Vandergheynst, Oscar Divorra Escoda
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/efef086a-a038-4b40-b310-26f8de272188

About this result

Related concepts (32)

Wavelet

A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases or decreases, and then returns to zero one or more times. Wavelets are termed a "brief oscillation". A taxonomy of wavelets has been established, based on the number and direction of its pulses. Wavelets are imbued with specific properties that make them useful for signal processing. For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly one tenth of a second.

Speech coding

Speech coding is an application of data compression to digital audio signals containing speech. Speech coding uses speech-specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream. Common applications of speech coding are mobile telephony and voice over IP (VoIP).

Discrete wavelet transform

In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time). Haar wavelet The first DWT was invented by Hungarian mathematician Alfréd Haar. For an input represented by a list of numbers, the Haar wavelet transform may be considered to pair up input values, storing the difference and passing the sum.

Related publications (1)

Locally Temporal Adaptive Transform Scheme for Sub-band Video Coding

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