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Related Concept Videos

Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Upsampling01:22

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...
Downsampling01:20

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...
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Related Experiment Videos

Perceptual video coding based on SSIM-inspired divisive normalization.

Shiqi Wang1, Abdul Rehman, Zhou Wang

  • 1Institute of Digital Media, School of Electronic Engineering and Computer Science, Peking University, Beijing 100871, China.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new perceptual video coding framework using divisive normalization. The method enhances visual quality and rate-SSIM performance compared to H.264/AVC.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Signal Processing
  • Image and Video Processing

Background:

  • Biological vision models perceptual sensitivity.
  • Divisive normalization effectively models this sensitivity.
  • Limited exploitation in video coding.

Purpose of the Study:

  • Develop a perceptual video coding framework.
  • Enhance visual quality and coding efficiency.
  • Leverage divisive normalization for video compression.

Main Methods:

  • Divisive normalization scheme at the macroblock (MB) level.
  • Normalization factors derived using Structural Similarity (SSIM) index.
  • MB-level perceptual mode selection and global quantization matrix optimization.

Main Results:

  • Significant gain in rate-SSIM performance.
  • Improved visual quality over H.264/AVC.
  • Validation through extensive simulations and subjective tests.

Conclusions:

  • The proposed framework effectively utilizes divisive normalization for video coding.
  • Achieves superior rate-SSIM performance and visual quality.
  • Offers a promising approach for perceptually optimized video compression.