Earlier quoted context omitted.
An image downscaled and then upscaled to its original size is effectively low-pass filtered where the degree of edge preservation is dictated by the kernel used in both cases. Are you saying low-pass filtering is bad for compression?
The word is "blur." Low-pass filtering is blurring. Is blurring good for compression? I don't know what that means. If the image size (not the file size) is held constant, a blurry image and a clear image take up exactly the same amount space in memory. Blurring is bad for quality. Our vision is sensitive to high-frequency stuff, and low-pass filtering is by definition the indiscriminate removal of high-frequency inf…
Consider lossless RLE compression schemes. In this case, would data with low or high variance compress better?
Now consider RLE against sets of DCT coefficients. See where this is going?
In general, having lower variance in your data results in better compression.
> Our vision is sensitive to high-frequency stuff
Which is exactly why we pick up HF noise so well! Post-processing houses are very often presented with the challenge of choosing just the right filter chain to maximize fidelity under size constraint(s).
> low-pass filtering is by definition the indiscriminate removal of high-frequency information
It's trivial to perform edge detection and build a mask to retain the most visually-meaningful high frequency data.