Discrete Cosine Transform: Algorithms, Advantages, Applications

By K. R. Rao, P. Yip, V. Britanak. 1990

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In Signal Processing

Citations in the corpus

Generating Images with Sparse Representations (2021, methodology): “JPEG (Wallace, 1992) and other popular lossy image compression methods use the discrete cosine transform (DCT) (Ahmed et al., 1974; Rao & Yip, 1990) to separate spatial frequencies of an image, and encode them with controllable resource budgets (e.g., the “quality” parameter).”
Compressing Convolutional Neural Networks in the Frequency Domain (2015, methodology): “Though highly related to the discrete Fourier transformation (DFT), DCT is often preferable for compression tasks because of its spectral compaction property where weights for most images tend to be concentrated in a few low-frequency components of the DCT [42].”
FcaNet: Frequency Channel Attention Networks (2020, background): “DCT has a strong energy compaction property [1, 30], so it could achieve high data compression ratio with high quality [2, 22].”
Photo Forensics From Rounding Artifacts (2020, background): “D DCT in step ( 5 ) is the most computationally demanding step of JPEG compression, a number of eicient DCT implementations are available [37].”
Analogue signal and image processing with large memristor crossbars (2017, methodology): “The DCT is a Fourier-related transform widely used in digital signal processing and image/video compression and processing 13,46 .”
The Design and Implementation of FFTW3 (2005, methodology): “real-symmetric (or anti-symmetric) data, otherwise known as DCTs and DSTs, types I–VIII [27], [51]–[53].”
Low-complexity transform and quantization in H.264/AVC (2003, methodology): “The DCT [10] is commonly used in block transform coding”
Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain (2006, methodology): “Similar to the definition of the energy packing efficiency (EPE) [24], we may define the variation estimation efficiency (VEE) as the performance criterion of variation estimation.”
Modern Image Quality Assessment (2006, methodology): “The discrete cosine transform (DCT) [29] has been adopted by many image and video compression standards, and efficient software and hardware implementations are easily accessible.”
Directional multiresolution image representations (2002, background): “Currently, the most popular choices for this purpose are the wavelet transform [175, 65, 103] and the discrete cosine transform [137].”
Fast multiplierless approximations of the DCT with the lifting scheme (2001, methodology): “HE discrete cosine transform (DCT) [1], [2] is a robust approximation of the optimal Karhunen-Loève transform (KLT) for a first-order Markov source with large correlation coefficient.”
Random projection in dimensionality reduction: applications to image and text data (2001, methodology): “DCT can be performed by simple matrix operations [23, 27]: an image is transformed to the DCT space and dimensionality reduction is done in the inverse transform by discarding the transform coefficients corresponding to the highest frequencies.”