Image compression with a multiresolution neural network

Abstract

Neural networks can be of benefit in many image compression schemes. However, any system is constrained by the performance of the paradigm on which it is based. For example, although neural networks have been shown to improve differential pulse code modulation (DPCM) image compression, the overall performance of the system is still limited by the performance of DPCM. In this work a multiresolution neural network (MRNN) filter bank has been created for use within a state-of-the-art subband-coding framework. A polyphase implementation and training algorithm is presented. A filter bank that can synthesize the signal accurately from only the reference coefficients will be well suited for low-bitrate coding where the detail coefficients are coarsely quantized. Thus, the low-pass channel of the MRNN filter bank is trained to recreate the signal accurately. The high-pass channel is trained for perfect reconstruction so that the MRNN filter bank will also be effective at high bitrates. This paper presents an analysis of the MRNN filter bank and its potential as a transform for coding. The MRNN filter bank has been used in place of a linear filter bank in the set partitioning in hierarchical trees (SPIHT) coder. The new filter bank shows advantages over the linear filter bank for coding at low bitrates, although its performance suffers at high bitrates. However, the results are encouraging and suggest that further work in this area warranted.Les réseaux de neurones peuventêtre bénéfiques en plusieurs configurations de compression d'image. Cependant, chaque système est contraint par les performances des paradigmes sur lesquels il est basé. Par exemple, même s'il aété montré que les réseaux de neurones améliorent la compression d'imageà modulation différentielle d'impulsion codée (DPCM), la performance globale du système est encore limitée par la performance du DPCM. Dans ce travail un banc de filtresà réseau de neurones multi-résolution (MRNN) aété créé pourêtre utilisé dans le cadre d'un codage sous-bande. Une implémentation multi-phase et un algorithme d'entraînement sont présentés. Un banc de filtres qui peut synthétiser le signal de manière exacte seulement a partir des coefficients de référence est très approprié pour un codageà faible taux pour lequel les coefficients de détail sont quantifiés de façon grossière. Ainsi, le canal passe-bas du banc de filtres MRNN est entraîné pour retrouver le signal de manière exacte. Le canal passe-haut est entraîné pour une reconstruction parfaite pour que le banc de filtres MRNN puisseêtre efficaceà des taux de bitsélevés. Cet article présente une analyse du banc de filtres et son potentiel comme transformation pour le codage. Le banc de filtres MRNN aété utiliséà la place d'un banc de filtres linéairesà codeur par partition des arbres hiérarchiques (SPIHT). Ce nouveau banc de filtres montre les avantages par rapport aux bancs de filtres linéaires pour les codagesà faibles taux de bits, bien que ses performances souffrentà des taux de bitsélevés. Cependant, les résultats sont prom...

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