A web extension to hide duplicate posts on pre-redesign Reddit.
When viewing a list of posts on reddit.com, the extension finds posts with the same URL or thumbnail and groups them together, showing only the first instance.
This is particularly useful when viewing an individual user's posts, as some users will post the same link or upload the same image to multiple subreddits. It can also be useful for viewing subscriptions or multireddits that aggregate posts from similar subreddits.
This extension is compatible with Reddit Enhancement Suite's "Never Ending Reddit" feature.
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| The extension automatically detects and hides duplicate posts. |
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| Click the "show"/"hide" link to reveal or hide duplicates. |
To detect duplicate thumbnails, the extension uses a perceptual hash algorithm to reduce each image to a 64-bit hash. Ideally, a perceptual hash algorithm should be insensitive to minor changes in an image — visually similar images should have similar hash values. This extension implements three different hash functions:
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DCT Hash: Scale the image to 32x32 and compute the direct cosine transform (DCT) of the pixel luminance values. Use the sign bits of the upper-left triangle of coefficients as the bits of the hash.
This hash has good accuracy as much of the perceptual information in an image is contained in the low-frequency components of the DCT. We can even use the hash to reconstruct an image that preserves many large-scale features of the original (see below).
However, the DCT can be slow to compute, as a naive implementation requires O(n3) multiplications for an n×n input matrix. We use an optimized fast-DCT algorithm to compute the DCT with fewer arithmetic operations.
Original DCT hash
visualization -
Difference Hash: Scale the image to 8x8 grayscale and compare adjacent pixels along a space-filling loop. Use the results of the comparison as the bits of the hash.
This hash is very fast to compute, but is sensitive to minor fluctuations in brightness across "flat" areas of the image where the difference in brightness is close to zero.
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Wavelet Hash: Scale the image to 32x32 and compute the discrete wavelet transform (DWT). We use the Haar wavelet given the small input size, and for simplicity. Take the sign bits of the upper 8x8 coefficients of the transformed matrix as the bits of the hash.
Using the fast wavelet transform algorithm, this hash is relatively fast to compute and improves on the difference hash by representing the gradient at different scales rather than purely locally. As with the DCT hash, it is possible to reconstruct something resembling the original image from the hash bits, although at a very low resolution.
Original Wavelet hash
visualization
For an interactive visualization of the different hash functions, see the perceptual hash demo.
Although searching for exact matches works surprisingly well, in order to further reduce false negatives we group thumbnails whose hash values differ by only a few bits. To find such almost-equal hashes, we use a BK-tree, a simple data structure adapted to discrete metric spaces such as Hamming space.
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Crop sensitivity For all currently supported image hash algorithms, cropping an image can radically change its hash value, which can cause the extension not to detect near-duplicates with slight framing differences.
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Unrelated duplicate thumbnails Sometimes unrelated pages for non-media sites will have the same thumbnail. For example, news articles from The Independent are sometimes wrongly identified as duplicates due to having the same generic "breaking news" thumbnail.
As a workaround, you can add domain overrides in the extension preferences to ignore thumbnails for affected domains.
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Variant URLs Sometimes different users will post the same URL with small variations, such as tracking parameters. Currently the extension only looks for exact duplicates when deduplicating by URL.
Merge icon by Freepik from www.flaticon.com (modified from original).