BACKGROUND

Many forms of information, such as audio and images, may be transformed into various representations thereof in the frequency domain, then transformed back to their original representation by an inverse transformation. A frequency domain representation of information has numerous applications in data processing. For example, image encoding algorithms generally compress image data by transforming image data into a frequency domain representation, as reduction of image data size can be readily accomplished by algorithms operating on frequency domain data.

Image data represented in the spatial domain may be represented in a variety of color spaces, including, commonly, the RGB color space and the YCbCr color space. In RGB color space, image data is separated into the red, green, and blue color components; in YCbCr color space, image data is separated into a luma component and two chroma components. Digital image data may readily be represented in either of these color spaces; for example, the commonly adopted MPEG encoder for video utilizes YCbCr. Furthermore, the similarly commonly adopted JPEG encoder for images utilizes YCbCr, as an intermediate step in converting image data from a spatial domain representation in RGB to a frequency domain representation by applying a discrete cosine transform (“DCT”).

In machine learning settings utilizing image-based learning models, training and inference datasets may be ready-made datasets of compressed images, but may alternatively be raw, uncompressed images captured by an image capture device such as a camera. In a system implemented for machine learning computation on massive datasets, computational resources may be distributed over a network in, for example, a cloud computing system to maximize power and availability. As uncompressed images are untenable for transportation over a network and training and inference computation, machine learning systems configured in this manner also rely on transformation and inverse transformation of images into and out of the frequency domain in order to compress image datasets for optimal transport, storage, and computation.

The transformation of image datasets into the frequency domain in the machine learning context is generally helpful in determining contextual information such as relative importance of image data, by the application of principles derived from, for example, frequency-based filtering. However, presently, no pipeline exists between coding of representations of images in the frequency domain and derivation of representations of images in the frequency domain for training and inference computations. Greater computational efficiency may be realized by leveraging image coding in the frequency domain for further applications in machine learning.

