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Convolution Properties II01:17

Convolution Properties II

585
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
585
Convolution Properties I01:20

Convolution Properties I

576
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
576
Passive Filters01:27

Passive Filters

974
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
974
Encoding01:19

Encoding

800
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
800
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

913
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
913

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Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment.

Mahdi S Hosseini, Yueyang Zhang, Konstantinos N Plataniotis

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    We developed HVS-MaxPol, a fast and accurate no-reference image sharpness assessment metric. It effectively evaluates image sharpness across various blurs, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Human-Computer Interaction

    Background:

    • No-reference Image Sharpness Assessment (NR-ISA) is crucial for image quality.
    • Current NR-ISA methods face challenges with computational cost and scalability.
    • Existing techniques struggle to adapt to diverse image blurs.

    Purpose of the Study:

    • To propose a novel Human Visual System (HVS) response in a convolutional filter form.
    • To develop an efficient and scalable NR-ISA metric.
    • To address the limitations of existing NR-ISA techniques.

    Main Methods:

    • Synthesized HVS response using Finite Impulse Response (FIR) derivative filters.
    • Implemented HVS filter with the MaxPol filter library for adjustable parameters.
    • Designed the HVS-MaxPol metric for minimal computational cost and high accuracy.
    • Validated the metric on synthetic and natural blur images, including a new FocusPath database.

    Main Results:

    • HVS-MaxPol demonstrated high correlation accuracy with image sharpness levels.
    • The metric exhibits scalability across synthetic and natural image blurs.
    • Achieved superior overall performance in speed, accuracy, and scalability compared to state-of-the-art NR-ISA metrics.

    Conclusions:

    • HVS-MaxPol offers an efficient and accurate solution for NR-ISA.
    • The proposed method effectively models HVS response for feature decomposition.
    • HVS-MaxPol provides a scalable and reliable tool for image quality assessment in various applications.