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Related Concept Videos

Upsampling01:22

Upsampling

305
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
305
Downsampling01:20

Downsampling

246
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
246
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

346
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
346
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

300
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
300
Sampling Methods: Overview01:06

Sampling Methods: Overview

490
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
490

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Related Experiment Video

Updated: Sep 6, 2025

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Suppressing Shot Noise Using Quadratic Variable Step-Size Quantization for the Initial Acquisition of Camera-Raw

Jianyu Lin, Jianxiao Qin, Shizhu Lu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 23, 2022
    PubMed
    Summary

    High-quality HDR images can be compressed to 8-9 bits/pixel without losing information by suppressing shot noise with quadratic quantization. This method achieves over 50% bitrate savings for raw image data.

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    Area of Science:

    • Digital imaging and signal processing.
    • Computational photography and image sensors.

    Background:

    • Modern digital cameras capture high dynamic range (HDR) images exceeding human visual perception.
    • Reducing raw image file sizes while preserving sensor-captured information is a key challenge.

    Purpose of the Study:

    • To demonstrate that extractable image information in HDR images can be preserved at lower bit-depths.
    • To reduce file size of raw image data through efficient quantization and compression.

    Main Methods:

    • Utilizing quadratic variable step-size quantization to suppress shot-noise-introduced entropy.
    • Introducing a parameter (quantizer step-size to shot noise amplitude ratio) for designing optimal quadratic quantizers.
    • Losslessly compressing quadratically quantized camera-raw image data.

    Main Results:

    • Preservation of extractable image information using only 8 to 9 bits/pixel.
    • Achieving over 50% bitrate savings compared to lossless compression of linearly quantized data.
    • Demonstrating significant bitrate reduction without information loss.

    Conclusions:

    • Quadratic quantization effectively suppresses shot noise, enabling significant data compression for HDR images.
    • The proposed method offers a highly attractive solution for reducing raw image file sizes.
    • Future hardware implementations of quadratic quantization in image sensors are recommended.