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    This study introduces a novel Dual-SIM quantizer (Dual-SIMQ) for visual data compression. It mimics human visual perception by using time-dependent processing for enhanced reconstruction quality.

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    Area of Science:

    • Computational Neuroscience
    • Image Processing
    • Artificial Intelligence

    Background:

    • Current quantization mechanisms process visual information irrespective of its temporal duration.
    • The human visual system enhances perception quality over time, a property not yet leveraged in compression.

    Purpose of the Study:

    • To introduce a novel coding/decoding mechanism, the Dual-SIM quantizer (Dual-SIMQ).
    • To mimic the human visual system's time-dependent processing for improved visual perception quality.
    • To develop a compression architecture that considers the temporal aspect of visual information.

    Main Methods:

    • Utilizing the leaky integrate-and-fire (LIF) model to convert visual stimuli into spike trains.
    • Employing two spike interpretation mechanisms (SIM): time-SIM for high-quality neural code interpretation and rate-SIM for simple spike counting.
    • Developing a compression architecture integrating these neuroscience-based models.

    Main Results:

    • The time-dependency of Dual-SIMQ automatically regulates the reconstruction accuracy of visual stimuli.
    • Dual-SIMQ demonstrates performance comparable to uniform quantization and approximates optimal non-uniform quantization.
    • Perceptual evaluation shows Dual-SIMQ yields higher reconstruction quality compared to state-of-the-art methods.

    Conclusions:

    • The proposed Dual-SIM quantizer effectively leverages temporal information for visual data compression.
    • Dual-SIMQ offers a novel approach to image and video compression, inspired by biological visual systems.
    • This method provides superior perceptual reconstruction quality while maintaining competitive numerical performance.