Abstract
This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered by the state-of-the-art quantization mechanisms that process the visual information regardless the duration of time it appears in the visual scene. We propose a compression architecture built of neuroscience models; it first uses the leaky integrate-and-fire (LIF) model to transform the visual stimulus into a spike train and then it combines two different kinds of spike interpretation mechanisms (SIM), the time-SIM and the rate-SIM for the encoding of the spike train. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by counting the spikes. For that reason, the proposed mechanisms is called Dual-SIM quantizer (Dual-SIMQ). We show that (i) the time-dependency of Dual-SIMQ automatically controls the reconstruction accuracy of the visual stimulus, (ii) the numerical comparison of Dual-SIMQ to the state-of-the-art shows that the performance of the proposed algorithm is similar to the uniform quantization schema while it approximates the optimal behavior of the non-uniform quantization schema and (iii) from the perceptual point of view the reconstruction quality using the Dual-SIMQ is higher than the state-of-the-art.
Highlights
C OMPRESSION is undoubtedly considered as one of the most important and necessary processing steps in Manuscript received May 8, 2020; revised December 1, 2020; accepted March 19, 2021
This paper shows that the Dual-SIMQ outperforms the capacity of a uniform scalar quantizer (USQ) without deadzone, it coincides with the performance of a USQ with deadzone and it approximates the optimal Lloyd-Max quantizer (LQ)
We aim to study the validity of the rate-distortion theory which is determined by the comparison of the rate-distortion approximation and the performance of the Dual-SIMQ when the distribution of the input signal is normal
Summary
C OMPRESSION is undoubtedly considered as one of the most important and necessary processing steps in Manuscript received May 8, 2020; revised December 1, 2020; accepted March 19, 2021. Date of publication April 9, 2021; date of current version April 16, 2021. The associate editor coordinating the review of this manuscript and approving it for publication was Dr Yun He. Images are highly correlated signals as they consist of a lot of redundancy. A lot of effort has been deployed to justify how to efficiently eliminate this redundancy while ensuring high reconstruction quality (lossy compression). The definition of redundancy is often associated with the sensitivity of the human visual system (HVS) to specific spatiotemporal frequencies. Understanding and modeling the visual perception seems to be very beneficial to the progress of compression algorithms [1], [2]
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