This paper deals with proposing new lifting scheme (HYBRID Algorithm) that is capable of preventing images and documents which are fraud through decomposing there in to the real colors value arrays (red, blue and green) to create retrieval keys for its properties and store it in the database and then check the document originality by retrieve the query image or document through the decomposition described above and compare the predicted color values (retrieval keys)  of the query document with those stored in the database. The proposed algorithm has been developed from the two known lifting schemes (Haar and D4) by merging them to find out HYBRID lifting scheme. The validity and accuracy of the proposed algorithm have been ev

In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.

A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of th

     In today's world, digital image storage and transmission play an essential role,where images are mainly involved in data transfer. Digital images usually take large storage space and bandwidth for transmission, so image compression is important in data communication. This paper discusses a unique and novel lossy image compression approach. Exactly 50% of image pixels are encoded, and other 50% pixels are excluded. The method uses a block approach. Pixels of the block are transformed with a novel transform. Pixel nibbles are mapped as a single bit in a transform table generating more zeros, which helps achieve compression. Later, inverse transform is applied in reconstruction, and a single bit value from the table is rem

    Today the Genetic Algorithm (GA) tops all the standard algorithms in solving complex nonlinear equations based on the laws of nature. However, permute convergence is considered one of the most significant drawbacks of GA, which is known as increasing the number of iterations needed to achieve a global optimum. To address this shortcoming, this paper proposes a new GA based on chaotic systems. In GA processes, we use the logistic map and the Linear Feedback Shift Register (LFSR) to generate chaotic values to use instead of each step requiring random values. The Chaos Genetic Algorithm (CGA) avoids local convergence more frequently than the traditional GA due to its diversity. The concept is using chaotic sequences with LFSR to gene

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. The major topic of this paper, is improve the compresses of still images by Multiwavelet based on estimation the high Multiwavelet coefficients in high frequencies sub band  by interpolation instead of sending all Multiwavelet coefficients. When comparing the proposed approach with other compression methods Good result obtained

Many recent satellite image compression methods depends on removing the spectral and spatial redundancies within image only , such these methods known as intra-frame(image) coding such as predictive and transformed based techniques , but these contributions needs a hard work in order to improve the compression performance also most of them are applied on individual data. The other trend is to exploit the temporal redundancy between the successive satellite images captured for the same area from different views, different sensors, or at different times, which will be much correlated and removing this redundancy will improve the compression performance and this principle known as inter-frame(image) coding .In this paper, a latest powerful

Some problems want to be solved in image compression to make the process workable and more efficient. Much work had been done in the field of lossy image compression based on wavelet and Discrete Cosine Transform (DCT). In this paper, an efficient image compression scheme is proposed, based on a common encoding transform scheme; It consists of the following steps: 1) bi-orthogonal (tab 9/7) wavelet transform to split the image data into sub-bands, 2) DCT to de-correlate the data, 3) the combined transform stage's output is subjected to scalar quantization before being mapped to positive, 4) and LZW encoding to produce the compressed data. The peak signal-to-noise (PSNR), compression ratio (CR), and compression gain (CG) measures were used t