In short, a wave with a high frequency has a short wavelength, and vice-versa.
The Discrete Cosine Transform is a variant of a 2-dimensional Fourier Transform. The 1-D version of a Fourier Transform is what we use to break a signal, like a sound wave, into its constituent frequencies. It takes as input the wave amplitude at various times, and returns amplitudes for various frequencies. If you were to take waves of those frequencies and amplitudes, and add them together, you would get back the original sound wave you started with. (I'm hand-waving away a bunch of details like phase, boundary conditions, undersampling, and overtones--but this is the general idea.)
You can make the Fourier Transform and its relatives deal with images the same way as sound, by pretending that the image is periodic, i.e. that you are tiling an infinite wall with copies of that image. You could create this same wall by overlaying waves of color on top of each other. The Fourier Transform will find these waves, the same way it found the frequencies for the sound.
With sound, low frequency = slow vibration = long wavelength (imagine an oscilliscope). High frequency = rapid vibration = short wavelengths. So if you were to try yo draw a picture using waves instead of a brush, you would use low frequencies for large things like a head. You would use medium frequencies to add smaller objects like eyes. You would use high frequencies to give small details, like hair or freckles, or the specific shape of a specific person's head.
The Discrete Cosine Transform is a variant of a 2-dimensional Fourier Transform. The 1-D version of a Fourier Transform is what we use to break a signal, like a sound wave, into its constituent frequencies. It takes as input the wave amplitude at various times, and returns amplitudes for various frequencies. If you were to take waves of those frequencies and amplitudes, and add them together, you would get back the original sound wave you started with. (I'm hand-waving away a bunch of details like phase, boundary conditions, undersampling, and overtones--but this is the general idea.)
You can make the Fourier Transform and its relatives deal with images the same way as sound, by pretending that the image is periodic, i.e. that you are tiling an infinite wall with copies of that image. You could create this same wall by overlaying waves of color on top of each other. The Fourier Transform will find these waves, the same way it found the frequencies for the sound.
With sound, low frequency = slow vibration = long wavelength (imagine an oscilliscope). High frequency = rapid vibration = short wavelengths. So if you were to try yo draw a picture using waves instead of a brush, you would use low frequencies for large things like a head. You would use medium frequencies to add smaller objects like eyes. You would use high frequencies to give small details, like hair or freckles, or the specific shape of a specific person's head.