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The study of a novel neuro-inspired quantizer in an integrated image compression architecture

Papanastasiou Anastasios-Panagiotis

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Year 2022
Type of Item Diploma Work
Bibliographic Citation Anastasios-Panagiotis Papanastasiou, "The study of a novel neuro-inspired quantizer in an integrated image compression architecture", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022
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In this thesis we study the performance of a of a quantizer proposed in 2021, called DUAL-SIM, within an integrated image compression architecture. This quantizer is motivated by the structure and functions of the human visual system and biological neurons which have the ability to convert and encode a visual stimulus into a sequence of discrete events called a spike train by which an image is quantized. This sequence of spikes allows a dynamic signal to be recorded, that is, a time-varying signal, and to record only the information that changes, giving less importance to what is static. The quantizer therefore exploits the characteristic of the visual system to distinguish static from dynamic information and to record only the events that emerge. In this work, the neuro-inspired quantizer is combined with state-of-the-art filters, such as the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT), in order to study both the trade-off between performance and distortion created by the proposed image compression architecture and the behaviour of the quantizer if it is introduced into a conventional image compression architecture environment, such as that of the JPEG and JPEG2000 algorithms.

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