Update bundles

bundles/version.json

@@ -1,10 +1,11 @@
 {
-  "updated_at": "2025-01-20T03:09:50.369708+00:00",
+  "updated_at": "2025-01-20T22:43:06.299491+00:00",
   "folders": [
     "custom_formats",
     "profiles",
     "regex_patterns",
     "group_tiers",
-    "dev_logs"
+    "dev_logs",
+    "wiki"
   ]
 }

bundles/wiki.json

@@ -0,0 +1,30 @@
+[
+  {
+    "_id": "EEi",
+    "content": "This metric is aimed at identifying and ranking release groups based on their propensity to release encodes that meet certain compression ratios, with particular focus on HEVC releases where optimal efficiency occurs in specific bitrate ranges. By ranking these groups, we effectively prioritize releases that maximize HEVC's compression capabilities while maintaining quality at minimal file sizes.\n\n## What is a Compression Ratio?\n\nA compression ratio is a (made up) metric that evaluates encodes against their sources. We express this as the encoded file size as a percentage of its source size (typically a remux or WEB-DL).\n\nFor example:\n\n| Movie   | Source (Remux) | Encode | Compression Ratio |\n| ------- | -------------- | ------ | ----------------- |\n| Movie A | 40 GB          | 10 GB  | 25%               |\n| Movie B | 30 GB          | 6 GB   | 20%               |\n| Movie C | 50 GB          | 15 GB  | 30%               |\n\n## Why Is This Important?\n\nUnderstanding compression ratios helps balance two competing needs: maintaining high video quality while minimizing file size. Modern codecs like HEVC have a \"sweet spot\" where they deliver excellent quality with significant size savings. Finding this optimal point is crucial because:\n\n- Storage and bandwidth are always limited resources\n- Going beyond certain bitrates provides diminishing quality returns\n- Different codecs have different efficiency curves\n- Release groups need clear standards for quality vs. size trade-offs\n\n## What Ratio is Best?\n\nThere's no one-size-fits-all answer when it comes to choosing the perfect compression ratio. The \"best\" ratio depends entirely on the end user's priorities and constraints. At 1080p:\n\n- Space-conscious users might prefer smaller files around 5-10% of source size, accepting quality trade-offs\n- Quality-focused users might push towards 30-40% of source size for more transparent results\n- Most users find a sweet spot in the middle, balancing quality and size\n  However, there are clear technical limits where going larger becomes pointless - this is where we get to our maximum ratios of 40% and 60% for 1080p and 2160p, respectively?\n\n## Why Set Maximum Ratios of 40% and 60%?\n\nThe compression ratio ceilings are set based on different factors for 1080p and 2160p content:\n\n### 1080p (40% Maximum)\n\nThe 40% ceiling for 1080p exists because we can roughly measure where h265 stops being more efficient than h264. We do this using two key video quality metrics:\n\n- **VMAF,** developed by Netflix, analyzes how humans perceive video quality and scores it from 0-100\n- **BD-Rate** tells us how much smaller one encode is compared to another while maintaining the same quality level\n\nUsing these tools together shows us that:\n\n- HEVC achieves 20-40% smaller files in the mid-bitrate range (~2-10 Mbps for 1080p)\n- These space savings are consistent across different quality levels\n- Beyond this point, both old and new codecs saturate at near transparent quality, making HEVC's advantages disappear\n- At ratios above 40%, x264 becomes the preferred choice as encodes are easier to produce and typically undergo more rigorous quality control\n\n### 2160p (60% Maximum)\n\nThe 60% ceiling for 2160p content is based on different considerations:\n\n- This is approximately where transparency becomes achievable for most 2160p content\n- Higher ratios provide diminishing returns in perceived quality\n- At this compression level, most content achieves VMAF scores above 95\n- Storage efficiency becomes particularly important at 2160p due to the larger base file sizes\n- The relationship between bitrate and perceived quality is non-linear, with improvements becoming increasingly subtle beyond this point\n\nGive these articles a read to better understand how VMAF and BD-Rate tell us how efficient a codec is:\n\n- https://medium.com/innovation-labs-blog/bjontegaard-delta-rate-metric-c8c82c1bc42c\n- https://www.mdpi.com/2079-9292/13/5/953\n\n## How Do We Apply This Index?\n\nThe ranking system works by calculating how close each Release Group / Streaming Service comes to achieving a user's desired compression ratio. This is done through a few key steps:\n\n1. **Delta Calculation**: We calculate the absolute difference (delta) between a group's average compression ratio and the target ratio. For example, if a group averages 25% compression and our target is 20%, their delta would be |25 - 20| = 5 percentage points.\n\n2. **K-means Clustering**: We use k-means clustering to automatically group release groups into tiers based on their deltas. K-means works by:\n   - Starting with k random cluster centers\n   - Assigning each group to its nearest center\n   - Recalculating centers based on group assignments\n   - Repeating until stable\n\n# Example Rankings\n\n## 1080p Examples\n\n### Example 1: Users prioritizing storage efficiency (10% target)\n\nUsers might choose this very aggressive compressionLine truncated
+    "last_modified": "2025-01-20T22:43:04.291029+00:00",
+    "title": "Encode Efficiency Index",
+    "slug": "EEi",
+    "author": "santiagosayshey",
+    "created": "2024-12-28",
+    "tags": [
+      "wiki",
+      "efficiency",
+      "encode"
+    ]
+  },
+  {
+    "_id": "GPPi",
+    "content": "## What are Golden Popcorns?\n\n**_Golden Popcorns_** are _very high quality encodes_, marked as such by one of the best private torrent trackers. These releases are manually reviewed by a dedicated, experienced team of _Golden Popcorn_ checkers. Golden Popcorns are the simplest way to quantify a subjective _best_ encode.\n\n## The Decision Engine\n\nThe Golden Popcorn Performance Index, or GPPI, is a calculated metric, pivotal to the [Transparent](../Profiles/1080p%20Transparent.md) profile's decision-making process. It's engineered to rank release groups based on their propensity to release a Golden Popcorn encode at any given resolution $r$.\n\n## Formula\n\nOn first glance, it seems the most obvious way to determine which release groups are most likely to release golden popcorns is to find their Golden Popcorn Ratio, i.e. The number of Golden Popcorns divided by the total number of encodes for any given resolution _r_.\n\nHowever, If we were to take Golden Popcorn ratio at face value, we might incorrectly prioritise a release group who has a high GP ratio, but a low number of encodes. On the opposite spectrum, if we take the raw number of Golden Popcorns for any group, we might incorrectly prioritise a group with a low GP ratio.\n\nSo instead, we multiply the number of Golden Popcorns at resolution $r$ for a given release group, by a factor of said release group's Golden Popcorn Ratio. This essentially limits both metrics as a factor of each other.\n\nFor any given resolution _r_, the GPPI is defined as:\n\n$$\n\\begin{aligned}\n\\text{GPPI}_r &= GPE_r \\cdot \\left( \\frac{GPE_r}{E_r} \\right) \\\\\n              &= \\frac{GPE_r^2}{E_r}\n\\end{aligned}\n$$\n\nWhere:\n\n- $\\text{GPPI}_r$ is the Golden Popcorn Performance Index at resolution $r$\n- $GPE_r$ is the number of Golden Popcorns at resolution $r$\n- $E_r$ is the total number of encodes at resolution $r$",
+    "last_modified": "2025-01-20T22:43:04.291029+00:00",
+    "title": "Golden Popcorn Performance Index",
+    "slug": "GPPi",
+    "author": "santiagosayshey",
+    "created": "2023-04-20",
+    "tags": [
+      "wiki",
+      "quality",
+      "encode"
+    ]
+  }
+]