By Madhuri A. Joshi, Mehul S. Raval, Yogesh H. Dandawate, Kalyani R. Joshi, Shilpa P. Metkar
ISBN-10: 148222822X
ISBN-13: 9781482228229
Image and video signs require huge transmission bandwidth and garage, resulting in excessive charges. the knowledge has to be compressed and not using a loss or with a small lack of caliber. therefore, effective snapshot and video compression algorithms play an important function within the garage and transmission of data.
Image and Video Compression: basics, options, and Applications explains the key innovations for picture and video compression and demonstrates their functional implementation utilizing MATLAB® courses. Designed for college students, researchers, and working towards engineers, the e-book offers either simple rules and genuine useful applications.
In an available means, the booklet covers easy schemes for photograph and video compression, together with lossless thoughts and wavelet- and vector quantization-based picture compression and electronic video compression. The MATLAB courses let readers to realize hands-on adventure with the innovations. The authors supply caliber metrics used to guage the functionality of the compression algorithms. additionally they introduce the trendy means of compressed sensing, which keeps an important a part of the sign whereas it truly is being sensed.
Read Online or Download Image and Video Compression: Fundamentals, Techniques, and Applications PDF
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Additional info for Image and Video Compression: Fundamentals, Techniques, and Applications
Sample text
The inverse of a unitary matrix is also unitary. The unitary matrix is diagonalizable: n U= ∑λ v v T i i i i=1 where {λ 1 , λ 2 , … , λ n } is a set of eigenvalues, and { v1 , v2 , v3 ⊃ vn } is the set of corresponding eigenvectors. This decomposition is known as the spectral decomposition of the unitary matrix. 3 Unitary Transform Let us first consider the case of a one-dimensional (1D) signal for understanding. The signal can be decomposed as x(t) = ∑ ∞n=0 Cn fn (t), where Cn is known as the nth coefficient of expansion for an orthonormal set Cn = ∫ tt+T x(t) fn (t)dt.
The number of 1’s in the binary image is counted to find a measure called the edge count. A higher value of edge count indicates large textural changes. 11 shows high-pass filtered images of a bubble and a hurricane. 2 indicates the edge count for images. 2 that the edge count covers a significantly large span of values. The high value of edge count indicates good texture activity, and vice versa. The proposed method uses three different values of step size based on the edge count. 3. 11 High-pass filtered images: (a) hurricane and (b) bubble.
These values of step size will be used in watermark embedding and extraction as described in the next section. 2 Watermark Embedding • • • • • • • • Divide the source image into nonoverlapping blocks of 8 × 8. Compute the forward DCT for each of these blocks. Scan the coefficients in a zigzag fashion. The DC coefficient and the first AC coefficient are used for watermark embedding in the block. These locations are shared between the encoder and decoder. The other coefficients in the block are left unaltered.