Related Experiment Video
Updated: Oct 16, 2025
11:23
Lensless Fluorescent Microscopy on a Chip
Published on: August 17, 2011
17.8K
A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images
Qunlin Chen1, Derong Chen1, Jiulu Gong1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Entropy (Basel, Switzerland)
|October 23, 2021
Summary
Block compressed sensing (BCS) image compression requires balancing sampling rate and bit-depth. New models optimize these parameters for efficient BCS, achieving near-optimal performance.
Area of Science:
- Signal Processing
- Image Compression
- Information Theory
Background:
- Block compressed sensing (BCS) is crucial for resource-constrained image sampling and compression.
- Optimizing BCS requires balancing sampling rate and quantization bit-depth under bit-rate constraints.
Purpose of the Study:
- To unify existing compressed sensing (CS) quantization frameworks.
- To propose novel bit-rate and optimal bit-depth models for BCS.
- To develop a general algorithm for selecting sampling rate and bit-depth.
Main Methods:
- Developed a unified framework for CS quantization.
- Proposed a bit-rate model based on generalized Gaussian distribution's information entropy.
- Introduced an optimal bit-depth model for CS measurements.
- Formulated a general algorithm for sampling rate and bit-depth selection.
Main Results:
- The proposed bit-rate model elucidates the relationship between bit-rate, sampling rate, and bit-depth.
- The optimal bit-depth model accurately predicts bit-depth for given bit-rates.
- The general algorithm demonstrated near-optimal rate-distortion performance.
Conclusions:
- The developed models and algorithm effectively optimize BCS parameters.
- Achieved significant performance improvements in uniform and predictive quantization frameworks for BCS.
Related Concept Videos
01:22Upsampling
353
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...
353
01:20Downsampling
303
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...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
303
01:10Reconstruction of Signal using Interpolation
401
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
401
01:26Linear Approximation in Frequency Domain
170
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....
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....
170