Energy Compaction on Graphs for Motion-Adaptive Transforms

2015 ◽  
Author(s):  
Du Liu ◽  
Markus Flierl
Keyword(s):  
Energy Compaction ◽  
Adaptive Transforms

Energy Compaction-Based Image Compression Using Convolutional AutoEncoder

2020 ◽  
Vol 22 (4) ◽  
pp. 860-873 ◽  
Author(s):  
Zhengxue Cheng ◽  
Heming Sun ◽  
Masaru Takeuchi ◽  
Jiro Katto
Keyword(s):  
Image Compression ◽  
Convolutional Autoencoder ◽  
Energy Compaction

scholarly journals Performance on ICI Self-Cancellation in FFT-OFDM and DCT-OFDM System

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Shilpi Gupta ◽  
Upena Dalal ◽  
Vishnu Narayan Mishra
Keyword(s):  
Bit Error Rate ◽  
Orthogonal Frequency Division Multiplexing ◽  
The Other ◽  
Frequency Division Multiplexing ◽  
Intercarrier Interference ◽  
Frequency Division ◽  
Ofdm System ◽  
Awgn Channel ◽  
Energy Compaction ◽  
Carrier To Interference Ratio

In orthogonal frequency division multiplexing (OFDM) system, the existence of frequency offset in AWGN channel affects the orthogonality among the subcarriers and consequently introduces the intercarrier interference (ICI). The paper investigates new ICI self-cancellation technique to mitigate the effect of ICI in FFT-OFDM and compares it to DCT based OFDM system in terms of bit error rate (BER) and carrier to interference ratio (CIR). The proposed method for group size three results in a significant 20 dB improved CIR in FFT-OFDM. In terms of BER, proposed ICI self-cancellation technique outperforms the other self-cancellation techniques in FFT-OFDM. Also, this paper investigates outperforming BER and CIR improvement by using DCT-OFDM without applying self-cancellation techniques, due to its energy compaction property.

Identification of multi-spectral palmprints using energy compaction by Hybrid wavelet

2012 ◽  
Author(s):  
H. B. Kekre ◽  
Rekha Vig ◽  
Saurabh Bisani ◽  
Tanuja Sarode ◽  
Pranay Arya ◽  
...  
Keyword(s):  
Energy Compaction

scholarly journals Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network

2010 ◽  
Vol 20 (4) ◽  
pp. 417-433 ◽  
Author(s):  
Jan Stolarek
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Energy Distribution ◽  
Lattice Structure ◽  
Optimization Method ◽  
New Method ◽  
Discrete Wavelet ◽  
Energy Compaction ◽  
High Pass

Improving energy compaction of a wavelet transform using genetic algorithm and fast neural networkIn this paper a new method for adaptive synthesis of a smooth orthogonal wavelet, using fast neural network and genetic algorithm, is introduced. Orthogonal lattice structure is presented. A new method of supervised training of fast neural network is introduced to synthesize a wavelet with desired energy distribution between output signals from low-pass and high-pass filters on subsequent levels of a Discrete Wavelet Transform. Genetic algorithm is proposed as a global optimization method for defined objective function, while neural network is used as a local optimization method to further improve the result. Proposed approach is tested by synthesizing wavelets with expected energy distribution between low- and high-pass filters. Energy compaction of proposed method and Daubechies wavelets is compared. Tests are performed using image signals.

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