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Upsampling

Upsampling

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...

Downsampling

Downsampling

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...

Sampling Methods: Overview

Sampling Methods: Overview

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...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Sampling Methods: Sample Types

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

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Related Experiment Video

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Sub-Sampled Cross-Component Prediction for Emerging Video Coding Standards.

Junru Li, Meng Wang, Li Zhang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 17, 2021

Summary

This summary is machine-generated.

This study introduces a simplified cross-component linear model (CCLM) for video compression. By using sub-sampling, it reduces operational complexity while maintaining excellent rate-distortion performance.

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Cross-Modal Multivariate Pattern Analysis

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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Video Compression Technologies
Digital Signal Processing
Machine Learning in Multimedia

Background:

Cross-component linear model (CCLM) prediction is effective for reducing inter-channel redundancies in video compression.
Traditional CCLM methods involve complex parameter derivation, increasing operational burden.

Purpose of the Study:

To investigate sub-sampled based cross-component correlation mining for simplifying CCLM.
To reduce the operational complexity and facilitate hardware/software design in video compression.

Main Methods:

Developed a sub-sampled based CCLM approach for video compression.
Designed sub-sampling ratios and positions exploiting spatial and inter-channel correlations.
Evaluated the method's simplicity and rate-distortion performance.

Main Results:

The proposed sub-sampled CCLM significantly reduces operational complexity.
The method demonstrates robustness in rate-distortion performance.
Achieved adoption in the Versatile Video Coding (VVC) and Audio Video Coding Standard (AVS3).

Conclusions:

Sub-sampled CCLM offers a simpler and efficient alternative for video compression.
The approach effectively balances complexity reduction with performance.
This method is a valuable contribution to modern video coding standards.