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Performance obtained (including the consumption of time dividedīy type of function used) are thoroughly presented in the paper. Implementation details, as well as the analysis of the compression The file too often with short readings or writings. Reading from thisįile and writing in this file run in large blocks, to avoid accessing When storing them in the compressed video file. Improvements are related to how the information bits are saved Value of 2n – 1 no longer complies with the divisibility by 8. More efficiently and resource-saving when n < 8 and implicitly the Main improvement is to replace fixed-sized ―video cubes‖ byĬreating variable-length parallelepipeds over time, giving up theįixed value of 8 for the ―video cube‖ dimension, which meansĬonsiderable reductions in the volume of this parallelepiped when Which brings certain improvements to the standard algorithm. Presents an original method of implementing a modified 3D-DCTĬompression algorithm, which is a variant derived from it and Quantization tables must be known by both the encoder and theĭecoder to be able to decompress the video information. The 3D-DCT algorithm that applies to it, the corresponding Independently encoded parallelepipeds, the most commonly formatīeing a ―video cube‖ of 888 size. Representing the entire video sequence, divided into smaller, This principle uses theĬoncept that a video sequence can be seen as a parallelepiped In adjacent frames (temporal correlation). The 3D-DCT is used in video compression when theĪpplied basic principle is the correlation between adjacent pixels inĪ frame and the correlation between pixels from the same position The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues. With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB.
#Zig zag coding in multimedia code
The proposed method is simple and reduces the complexity of the convolutional techniques.Ĭoding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process.
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The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. Compression techniques even help for smaller storage requirements. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. The main purpose of the proposed system is to use the bandwidth effectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. Large bandwidth and storage are required for the exchange of data and storage, respectively. With the advent of technology, a huge amount of data is being transmitted and received through the internet.
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