Reduction of discrete cosine transform/quantisation/inverse quantisation/inverse discrete cosine transform computational complexity in H.264 video encoding by using an efficient prediction algorithm

2009 ◽  
Vol 3 (4) ◽  
pp. 177-187 ◽  
Author(s):  
C.-L. Hsu ◽  
C.-H. Cheng
Keyword(s):  
Computational Complexity ◽  
Discrete Cosine Transform ◽  
Video Encoding ◽  
Prediction Algorithm ◽  
Cosine Transform ◽  
Inverse Discrete Cosine Transform ◽  
Efficient Prediction

A Dual Image Scrambling Method Based on Discrete Cosine Transform

2014 ◽  
Vol 556-562 ◽  
pp. 4722-4725
Author(s):  
Qiu Dong Sun ◽  
Jian Cun Zuo ◽  
Yu Feng Shao ◽  
Lin Gui
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Image ◽  
Original Image ◽  
Image Scrambling ◽  
Visual Evaluation ◽  
Space Domain ◽  
Cosine Transform ◽  
Inverse Discrete Cosine Transform ◽  
Dual Image ◽  
Arnold Transformation

Although the general random scrambling based on pixel can achieve a good chaotic effect, but it can not change the histogram of a digital image. We introduce the random scrambling into the domain of discrete cosine transform (DCT) of image and scramble the coefficients of DCT to improve the performance of scrambling. Firstly, we did 2-D discrete cosine transform to the original image. Secondly, we scanned the coefficients matrix of DCT by Zig-Zag scanning to get a 1-D sequence, and then we scrambled this sequence by 1-D random scrambling algorithm. Thirdly, we did inverse Zig-Zag scanning to the scrambled sequence and reconstructed the scrambled image from the chaotic coefficients matrix by 2-D inverse discrete cosine transform. Finally, to further improve the scrambling degree of the reconstructed result, we scrambled it again in space domain to gain the encryption image. Experiments show that this algorithm is effective at visual evaluation and is more stable in scrambling degree than Arnold transformation.

Research on 2-D Integer SDCT Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 1571-1575
Author(s):  
Hai Yan Tang ◽  
Wen Bang Sun ◽  
De Bao Zhang ◽  
Yan Chang
Keyword(s):  
Computational Complexity ◽  
Discrete Cosine Transform ◽  
Basic Principle ◽  
Total Space ◽  
Floating Point ◽  
Theoretic Analysis ◽  
Matrix Operation ◽  
Cosine Transform ◽  
Space Characteristic ◽  
Transform Matrix

Nowadays, 2-D DCT is applied widely. But the transform matrix of DCT is expressed with floating-point numbers, so the computational complexity is high and more system resources are occupied. In addition, the 2-D DCT is accomplished by operating 1-D DCT to the rows and columns of 2-D data successively, which cannt embody the total space characteristic of 2-D transform well. To overcome these drawbacks, 2-D integer SDCT (Sub-matrix Discrete Cosine Transform) was proposed in the paper. First, several matrix operation methods were defined. Then, the basic principle of 2-D integer SDCT was deduced in detail. The theoretic analysis show that 2-D integer SDCT is easy to comprehend, convenient to operate, and simplifies the calculation of 2-D DCT.

Sign in / Sign up

Export Citation Format

Share Document