Bits Per Pixel (bpp)
0 Followers
Recent papers in Bits Per Pixel (bpp)
Compression is the art of representing the information in a compact form rather than in its original or uncompressed form. In other words, using the data compression, the size of a particular file can be reduced. This is very useful when... more
Compression is the art of representing the information in a compact form rather than in its original or uncompressed form. In other words, using the data compression, the size of a particular file can be reduced. This is very useful when processing, storing or transferring a huge file, which needs lots of resources. If the algorithms used to encrypt work properly, there should be a significant difference between the original file and the compressed file. When the data compression is used in a data transmission application, speed is the primary goal. The speed of the transmission depends on the number of bits sent, the time required for the encoder to generate the coded message, and the time required for the decoder to recover the original ensemble. In a data storage application, the degree of compression is the primary concern. Compression can be classified as either lossy or lossless.
Image compression is a key technology in the transmission and storage of digital images because of vast data associated with them. This research suggests an effective approach for image compression using Stationary Wavelet Transform (SWT) and Vector Quantization which is a Linde Buzo Gray (LBG) vector quantization in order to compressed input images in four phases; namely preprocessing, image transformation, zigzag scan, and lossy/lossless compression. Preprocessing phase takes images as input, so that the proposed approach resize the image in accordance with the measured rate of different sizes to (8 × 8) And then converted from (RGB) to (gray scale). Image transformation phase received the resizable gray scale images and produced transformed images using SWT. Zigzag scan phase takes as an input the transformed images in 2D matrix and produced images in 1D matrix. Finally, in lossy/lossless compression phase takes 1D matrix and apply LBG vector quantization as lossy compression techniques and other lossless compression techniques such as Huffman coding and arithmetic coding. The result of our approach gives the highest possible compression ratio and less time possible than other compression approaches. Our approach is useful in the internet image compression.
Image compression is a key technology in the transmission and storage of digital images because of vast data associated with them. This research suggests an effective approach for image compression using Stationary Wavelet Transform (SWT) and Vector Quantization which is a Linde Buzo Gray (LBG) vector quantization in order to compressed input images in four phases; namely preprocessing, image transformation, zigzag scan, and lossy/lossless compression. Preprocessing phase takes images as input, so that the proposed approach resize the image in accordance with the measured rate of different sizes to (8 × 8) And then converted from (RGB) to (gray scale). Image transformation phase received the resizable gray scale images and produced transformed images using SWT. Zigzag scan phase takes as an input the transformed images in 2D matrix and produced images in 1D matrix. Finally, in lossy/lossless compression phase takes 1D matrix and apply LBG vector quantization as lossy compression techniques and other lossless compression techniques such as Huffman coding and arithmetic coding. The result of our approach gives the highest possible compression ratio and less time possible than other compression approaches. Our approach is useful in the internet image compression.
This paper proposes particle swarm optimization method to design M channel near perfect reconstruction pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest entropy based coder. To achieve... more
This paper proposes particle swarm optimization method to design M channel near perfect reconstruction pseudo QMF banks used in transforming stage of image coder. The filter bank is designed to have highest entropy based coder. To achieve high energy compaction and least distortion, design problem is formulated as a combination of the coding gain, low dc leakage conditions and stopband attenuation. For distortion free signal representation perfect reconstruction and good visual quality measures are imposed as constraints. The design problem is solved using (particle swarm optimization) PSO technique for minimizing filter tap weights. The technique find out solution by searching feasible solutions that achieve the best solution for the objectives criteria mentioned above. The performance of this optimization technique in filter bank design for image compression is evaluated in terms of both objective quality via coding gain, PSNR measures and subjective visual quality measure using both JPEG baseline image coder and an Embedded Zerotree Wavelet (EZW) coder. For comparison same test images for approximately same conditions and characteristics are used to measure compression ratio and peak signal to noise ratio (PSNR) for lower bit rates.