Hyper-chaos has more than one positive Lyapunov exponents and it has more complex dynamical characteristics than chaos Hence it becomes a better choice for secure image encryption schemes In this paper, we propose a new image encryption scheme integrated with compression simultaneously Specifically, we first use 2D hyper-chaos discrete nonlinear dynamic system to shuffle the plain image, and then we apply Chinese remainder theorem (well known in number theory) to diffuse and compress the shuffled image, simultaneously This new scheme can be used to change the plain image information drastically and compress the plain image with a given compression ratio k , which is most crucial in multimedia transmission Theoretical and experimental analyses both confirm the security and the validity of the proposed algorithm

A novel image encryption–compression scheme using hyper-chaos and Chinese remainder theorem

This paper introduces a new method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA), which is a widely used method to construct a codebook in vector quantization. Reduced comparison search detects the activity of the code vectors and utilizes it on the classification of the training vectors. For training vectors whose current code vector has not been modified, we calculate distances only to the active code vectors. A large proportion of the distance calculations can be omitted without sacrificing the optimality of the partition. The new method is included in several fast GLA variants reducing their running times over 50% on average.

A fast exact GLA based on code vector activity detection

In this paper, a color image retrieval method based on the primitives of color moments will be proposed. First, an image is divided into several blocks. Then, the color moments of all blocks are extracted and clustered into several classes. The mean moments of each class are considered as a primitive of the image. All primitives are used as features. Since two different images may have different numbers of features, a new similarity measure is then proposed. To demonstrate the effectiveness of the proposed method, a test database from Corel is used to compare the performances of the proposed method with other existing ones. The experimental results show that the proposed method is better than others.

https://ir.nctu.edu.tw/bitstream/11536/28350/1/000180791200008.pdf

Color Image Retrieval Based on Primitives of Color Moments

In this paper we propose a method to automatically identify birds from the sounds they generate. First, each syllable corresponding to a piece of vocalization is segmented. For each syllable, the averaged LPCCs (ALPCC) and averaged MFCCs (AMFCC) over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA) is exploited to increase the classification accuracy at a lower dimensional feature vector space. In our experiments, AMFCC usually outperforms ALPCC. If a codebook consisting of several representative feature vectors is used to model the syllables of the same bird species, the average classification accuracy is 87% for the recognition of 420 bird species.

Automatic Recognition of Bird Songs Using Cepstral Coefficients

The design of the optimal codebook for a given codebook size and input source is a challenging puzzle that remains to be solved. The key problem in optimal codebook design is how to construct a set of codevectors efficiently to minimize the average distortion. A minimax criterion of minimizing the maximum partial distortion is introduced in this paper. Based on the partial distortion theorem, it is shown that minimizing the maximum partial distortion and minimizing the average distortion will asymptotically have the same optimal solution corresponding to equal and minimal partial distortion. Motivated by the result, we incorporate the alternative minimax criterion into the on-line learning mechanism, and develop a new algorithm called minimax partial distortion competitive learning (MMPDCL) for optimal codebook design. A computation acceleration scheme for the MMPDCL algorithm is implemented using the partial distance search technique, thus significantly increasing its computational efficiency. Extensive experiments have demonstrated that compared with some well-known codebook design algorithms, the MMPDCL algorithm consistently produces the best codebooks with the smallest average distortions. As the codebook size increases, the performance gain becomes more significant using the MMPDCL algorithm. The robustness and computational efficiency of this new algorithm further highlight its advantages.

Minimax partial distortion competitive learning for optimal codebook design

Image compression by vector quantization: a review focused on codebook generation

In Vector Quantization (VQ) of subbands, a specialized codebook is required for each subband Thus, the codebook generation is time-consuming when iterative algorithm such as Linde-Buzo-Gray (LBG) algorithm is used We propose a Progressive Constructive Clustering (PCC) algorithm as a non-iterative technique to design the codebook for vector quantization Subband coding at 057 bit per pixel indicates that the codebooks generated by the PCC and LBG algorithms both yield the same distortion However, using the PCC algorithm, the codebook can be developed in about 2 percent of the time required by LBG algorithm

N. Akrout

Papers

Image compression by vector quantization: a review focused on codebook generation

A fast algorithm for vector quantization: application to codebook generation in image subband coding