https://github.com/cloudinary/ssimulacra2?tab=readme-ov-file... shows a higher correlation with human responses across 4 different datasets and correlation metrics for one.

also see https://jon-cld.s3.amazonaws.com/test/ahall_of_fshame_SSIMUL... which is an ab comparison of a lot of images where it gives 2 versions, one preferred by ssimulacra, the other preferred by vmaf

The authors of the metric found some cases where it works better is not the same thing as it being widely considered to be better. When it comes to typical video compression and scaling artifacts VMAF does really well. To prove something is better than VMAF on video compression it should be compared on datasets like MCL-V, BVI-HD, CC-HD, CC-HDDO, SHVC, IVP, VQEGHD3 and so on (and of course Netflix Public).

TID2013 for example is an image dataset with many artifacts completely unrelated to compression and scaling.

- Additive Gaussian noise - Additive noise in color components is more intensive than additive noise in the luminance component - Spatially correlated noise - Masked noise - High frequency noise - Impulse noise - Quantization noise - Gaussian blur - Image denoising - JPEG compression - JPEG2000 compression - JPEG transmission errors - JPEG2000 transmission errors - Non eccentricity pattern noise - Local block-wise distortions of different intensity - Mean shift (intensity shift) - Contrast change - Change of color saturation - Multiplicative Gaussian noise - Comfort noise - Lossy compression of noisy images - Image color quantization with dither - Chromatic aberrations - Sparse sampling and reconstruction

Doing better on TID2013 is not really an indication of doing better on a video compression and scaling dataset (or being more useful for making decisions for video compression and streaming).