2D convolution and 2D transforms, such as wavelet transform and discrete cosine transform (DCT), are widely used in image and video processing. To reduce the computation complexity, these algorithms are often implemented in two separable passes of 1D processing (e.g., row-wise processing followed by column-wise processing). For example, the number of multiplications of a direct N x N 2D DCT is ...

Autoencoders (AE) are essential in learning representation of large data (like images) for dimensionality reduction. Images are converted to sparse domain using transforms like Fast Fourier Transform (FFT) or Discrete Cosine Transform (DCT) where information that requires encoding is minimal. By optimally selecting the feature-rich frequencies, we are able to learn the latent vectors more robus...

A new hybrid two-stage electrocardiogram (ECG) signal compression method based on the modified discrete cosine transform (MDCT) and discrete wavelet transform (DWT) is proposed. The ECG signal is partitioned into blocks and the MDCT is applied to each block to decorrelate the spectral information. Then, the DWT is applied to the resulting MDCT coefficients. Removing spectral redundancy is achie...

We describe and apply a flexible, adaptive cosine packet transform to separate audio sources from instantaneous, underdetermined audio mixtures by time-frequency masking. Previously studied adaptive transform schemes have two main drawbacks: the signal can only be partitioned into dyadic intervals, and the profiles of the overlapping windows are often very short, thus tapering off very quickly....

Gigantic size of multimedia data and images being generated stored and transmitted across the digital web today, which has raised new research concerns in computing field. One of such concerns is compression of such data with minimal quality distortion. In recent work, the hybrid wavelet transforms (HWT) are proven to be better in image compression as compared to the constituent transforms cons...

Image compression is a method through which we can reduce the storage space of images, videos which will helpful to increase storage and transmission process’s performance. In image compression, we do not only concentrate on reducing size but also concentrate on doing it without losing quality and information of image. In this paper, we present the comparison of the performance of Discrete cosi...

Video compression is a process of eliminating redundancy and irrelevant information for efficient storage and transmission. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the popular transforms used for efficient compression. DCT has high energy compaction and requires less computational resources. In this paper, a novel method is proposed combining DCT, DWT and Arithm...

Abstract – Discrete Wavelet Transform (DWT) foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. Such algorithms lead into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly u...

Advance medical imaging requires storage of large quantities of digitized clinical data. Due to the bandwidth and storage limitations, medical images must be compressed before transmission and storage. Diagnosis is effective only when compression techniques preserve all the relevant and important image information needed. There are basically two types of image compression: lossless and lossy. L...

FFTW is an implementation of the discrete Fourier transform (DFT) that adapts to the hardware in order to maximize performance. This paper shows that such an approach can yield an implementation that is competitive with handoptimized libraries, and describes the software structure that makes our current FFTW3 version flexible and adaptive. We further discuss a new algorithm for real-data DFTs o...