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{
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"_id": "Architecture Overhaul",
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"content": "Hey @everyone, here's a small update on what I've been working on lately:\n\nAs the project has grown bigger, it's gotten quite difficult to keep track of and manage a billion different custom formats, quality profiles, etc. To help improve development productivity, I've planned a complete overhaul of Dictionarry's architecture. This starts with separating things into modules - namely a separate database which powers the website and the profilarr tool.\n\nNext up is standardizing the actual entries inside the database. The biggest issue in development right now is making / editing / updating the same thing multiple times. If you have the same regex pattern for multiple CFs, it needs to be updated for each one of them. Quality profiles across different apps have miniscule differences in syntax (eg. web-dl in radarr vs web in sonarr), which means we need multiple files with tiny differences.\n\nWorking in this system is extremely error prone and time consuming. To fix this, I'm creating a standard unique to dictionarry based on a **single definition format**, i.e. Regex patterns, Custom Formats and Quality Profiles are defined once, and repeated in other places using foreign keys. I don't know exactly _how_ this will look, but the plan is simplicity above all. Outside of improving productivity, I hope this standard helps encourage people who feel less confident with custom formats / quality profiles make more intuitive changes to their own setups.\n\nNow, the problem with this new and improved standard is - the arrs won't be able to read the files anymore. Solution: A compiler! This is where the fun begins; we take our simple, easy-to-develop-for files and push them through the compiler. Out pops the required syntax, with those weird naming rules (web-dl for radarr, web for sonarr), without the developer needing to ever worry about it!\n\nHere's a canvas page I made in Obsidian which visualizes this architecture:\n\n\n\n# Profile Selector\n\nHere's an updated look at the new profile selector (WIP) in action. I'll leave explaining the selection algorithm for another day (because I'm still not quite happy with it), but I think it's still pretty cool to look at as is.\n\n",
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.976043+00:00",
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"title": "Architecture Overhaul",
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"slug": "architecture_overhaul",
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"author": "santiagosayshey",
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@@ -15,7 +15,7 @@
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{
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"_id": "Modular Choices",
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"content": "Hey @everyone, here's a small (but very important) post on the new update system!\n\n## Current Profilarr\n\nCurrently, there is 0 support for updates in Profilarr. This is obviously not ideal; it's a nightmare to keep up to date with changes and almost certainly breaks any custom changes you make.\n\n## Profilarr v1\n\nUsers will be able to view incoming and outgoing changes, as well as resolve any conflicts between the two. To achieve this, a user friendly GUI has been built on top of Git's merge functionality and allows fine control over what should be merged / ignored. More specifically, this functionality allows us to make custom changes and choose to retain them once a new update comes around.\n\n- As an example, let's say you've made the Dolby Vision custom formats negative because your TV doesn't support it. A new update has come out which shuffles around HDR scores, and this leads to a merge conflict between the two custom format scores.\n- In the settings page, you can choose to accept the incoming change or retain your local changes. Profilarr will 'remember' your choice and stop prompting you to update this custom format until a new update comes out, in which case, the situation repeats. Keep local or accept incoming.\n\n### Settings Page\n\nProfilarr now includes a dedicated page for 'Sync Settings'. It allows you to link / unlink a database repository, view and change branches as well as deal with incoming / outgoing changes and their conflicts. This page has been planned for developers too; you can add an authenticated github dev token to your environment and you have the ability to make changes directly to Profilarr's database (not to stable, obviously).\n\n# Beta Release\n\n- Still not quite ready yet, but I'm working hard to get it out! Stay tuned :hearts:\n\nHere's a screenshot of this new Conflict Resolver in action (Ignore the date modified row, it will be removed for actual use)\n\n",
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.976043+00:00",
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"title": "Modular Choices",
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"slug": "modular_choices",
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"author": "santiagosayshey",
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@@ -29,7 +29,7 @@
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{
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"_id": "Profile Selector v3",
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"content": "hey @everyone , thought I'd make a channel to share some development logs.\n\nI've been feeling pretty inspired code wise the past few days, so I've actually made some progress despite saying I would take a break...\n\nAnyways, after designing Profile Selector v3 in Figma for the past couple months, I started work on actually implementing it. Let me tell you that drawing shapes is much, much easier than coding them. After a couple days of regretting not paying attention in high school trigonometry, I have the basic functionality in place! We have three data points which represent each of the requirements - quality, efficiency, compatibility. The user can select points on each of the axes, and each combination is used to recommend a profile. It's not hooked up to the database yet, so random strings are being used as a placeholder.\n\nThe good thing about this design is that it's really modular. Once I finish the 'beginner' version of it, I'll be able to add an advanced mode which can be used to select any kind of requirement. Resolution, HDR, Audio, etc.\n\nHere's how it looks right now (obvious disclaimer that final version will look much much better):\n\n\n\nHere's a funny tidbit from development:\n\nI tried writing some animation styling to make the inner polygon look like its stretching (as opposed to instant, static movement). It didn't quite work..\n\nBehold: Frankenstein's Triangle.\n\n",
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.976043+00:00",
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"title": "Profile Selector v3",
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"slug": "profile_selector_v3",
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"author": "santiagosayshey",
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@@ -43,7 +43,7 @@
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{
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"_id": "Profile Tweaks",
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"content": "Hey @everyone, I've been hard at work on the next Profilarr version over the past few weeks and have new stuff to show off!\n\nThe profiles we make are meant to be (really good) starting points, not a strict standard on what you _should_ be grabbing. Up until now, profiles existed as singular entities that don't respect custom changes. Merge conflict resolution was a big step in the right direction for this (read more in the last dev log), but it's a bit more hands on, and not something I expect most people to engage with.\n\nEnter 'Profile Tweaks'. These are simple check boxes you can enable / disable and are unique to YOUR profiles. They will ALWAYS be respected, regardless of what updates we make to the base profile. For now, these tweaks include:\n\n- Prefer Freeleech\n- Allow Prereleases (CAMS, Screeners, etc)\n- Language Strictness\n- Allow Lossless audio\n- Allow Dolby Vision without Fallback\n- Allow bleeding edge codecs (AV-1, H266)\n\n(Some are only available for specific profiles, eg lossless audio for 1080p Encode profiles).\n\nIf anyone has any tweak ideas (even super specific ones), please let me know and I'll work on getting it integrated! Here's an image of the Tweaks Tab:\n\n## Profilarr Progress\n\n- Progress is steady, I've been working on it every day since my semester ended. It's taken way, way longer than I've expected (sorry!) but I'm happy with how it's starting to look.\n- Git integration is complete and working, but needs lots of testing.\n- Data modules (custom formats, regex patterns, quality profiles) are complete and fully implement the existing logic from Radarr / Sonarr.\n- I am currently in the progress of porting existing data to the new database (https://github.com/Dictionarry-Hub/database/tree/stable) in the new profilarr standard format. This is going to take a while, as I have to write descriptions, add tags, test cases, etc.\n- Finally, I am starting to work on the compilation engine (https://discord.com/channels/1202375791556431892/1246504849265266738/1272756617041154049) and the import module. Once these things are complete, and I'm confident we won't run into massive bugs, I'll release a beta docker image. ETA? I really don't know, but I'm working as hard as I can.\n\nIf anyone has any tweak ideas (even super specific ones), please let me know and I'll work on getting it integrated! Here's an image of the Tweaks Tab:\n\n",
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.976043+00:00",
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"title": "Profile Tweaks",
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"slug": "profile_tweaks",
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"author": "santiagosayshey",
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{
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"_id": "Shiny New Stuff",
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"content": "hey @everyone, hope you guys are well. Here's another update!\n\n# Motivation\n\nI've been really struggling to work on this project for a few months now - I'll finally get some time at the end of the week but feel completely unmotivated to work on it for more than an hour. Well... after cracking the architecture problem last week and seeing all the support from you guys, I've felt especially motivated to dive back in.\n\n# Profilarr v2 (not really v2 but it sounded cool)\n\nProfilarr is getting some really nice upgrades. Here's an outline of the most important ones:\n\n## It's now a full stack application.\n\nThis means we have a frontend: a site that users can visit to adjust, import, and export regexes, custom formats, and quality profiles. It's built in a way that aims to 'remaster' how it's implemented in Radarr/Sonarr. All the existing functionality is there, but with some really nice quality of life features:\n\n- **Single definition format**: As outlined in the previous dev log, Profilarr's version of this system will use a single definition format. Notably, this allows you to set regex patterns ONCE, then add that regex as a condition inside a custom format.\n- **Sorting and Filtering**: You can now sort and filter items by title, date modified, etc.\n- **Exporting/Importing**: The standard format now allows _everyone_ to import/export regexes, custom formats, and quality profiles freely - no need to query APIs to do this anymore.\n- **Syncing**: Instead of clogging up everyone's arrs with unused custom formats, the sync functionality now only imports _used_ items.\n- **Mass selection**: You can mass select items to import/export/sync/delete.\n- **Tags**: Instead of manual selection, you can set tags on specific custom formats/quality profiles that should be synced. This works similar to how Prowlarr uses tags to selectively sync indexers. Since we are also using the same database for the website, tags can also be used for little tidbits of information too. Like where a release group is an internal at!\n- **Testing**: Developers can now permalink regexes to regex101. This makes it really easy to develop and test simultaneously.\n- **Descriptions**: You can now explain what specific items are for. No need to look it up on the website to see what it does.\n\n## Backend Improvements\n\nThe backend is essentially what Profilarr is right now - a tool to sync some JSON files to your arrs. However, this also has some major improvements:\n\n- **Git integration**: You can select a remote repository to connect to and:\n - Add, commit, and push files; branch off; merge into. This isn't that useful for end users, but I cannot stress enough how much time and suffering this has saved me. Being able to revert regex/custom format/quality profiles to the last commit is my favorite thing I've ever coded.\n - **Branching**: You can have different branches for different things. Of course, this is useful for development, but it also allows you to do things like: separate setups for Radarr/Sonarr/Lidarr. Most importantly, it allows us developers to set stable, dev, and feature branches.\n - **Pulling**: You can now pull in changes from specific branches from a remote repository. You can view differences and decide if you want to pull these changes in. You can set it to be automatic and only alert on merge conflicts (you change something, but an incoming change for that item exists as well). You can choose to get the most stable branch or the latest features merged into develop.\n - **External sources**: You can set your own repo of regexes, custom formats, and quality profiles and share it with whoever you want. As I mentioned in my last dev log, I'll be working on a compiler to convert our standard Profilarr format with the existing arr format. The really cool thing about this is it works both ways. This means the git integration + compiler will allow you to use Profilarr with the trash guides. It'll probably take some tweaking, but I know it's definitely possible now.\n\n## Containerisation\n\nProfilarr will FINALLY be dockerised.\n\n# Development\n\nWith these changes in place, it has massively improved and sped up development. Working in a proprietary tool now allows me the freedom to just implement a feature whenever I want to. Want to filter custom formats with the release tier tag? Boom, implemented. Want to auto-apply scores to custom formats in quality profiles based on tags? Boom, implemented.\n\n## Machine Learning\n\nThis part is mostly speculation and rambling - nothing concrete yet. I really want to incorporate some kind of AI help into Profilarr. A button you can press to auto-generate regex or a custom format. I've read countless Reddit posts of someone unfamiliar with regex/custom formats/profiles asking for help in trying to learn. \"How do I write a custom format that matches x265 releases under size x?\" It's so easily solved using AI.\n\nI want to implement this one day, I just Line truncated
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.977044+00:00",
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"title": "Shiny New Stuff",
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"slug": "shiny_new_stuff",
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"author": "santiagosayshey",
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{
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"_id": "Vision Almost Realised",
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"content": "Hey @everyone, small log for today!\n\n```bash\n$ python profile_compile.py 'profiles/1080p Encode.yml' '1080p Encode (sonarr - master).json' -s\nConverted profile saved to: 1080p Encode (sonarr - master).json\n\n$ python importarr.py\nImporting Quality Profiles to sonarr : Master\nUpdating '1080p Encode' quality profile : SUCCESS\n```\n\nThese two commands are the culmination of the architecture overhaul I talked about in August: https://discord.com/channels/1202375791556431892/1246504849265266738/1272756617041154049. The Profilarr standard format _**works**_. A typical profile is now about 300 lines (down from 1000 each for radarr / sonarr), is able to be compiled from PSF to Radarr OR Sonarr (and back!). Regex patterns allow format resolution, so no more editing the same thing 5, 10... 20 times.\n\nI'm currently in the process of hooking up the database to the new website, and that's looking pretty cool too. I cannot even explain how good it feels to be able to edit a profile once inside Profilarr, push those changes directly from Profilarr, have those changes reflected as incoming changes for end users, and as updated information on the website all in one fell swoop.\n\nIt's taken a huge effort the past 4 months, and I still have to actually connect it to the backend, but I'm fairly happy with how it's turned out. The changes won't be all that evident right away for you guys, but it's going to save me (and anyone who wants to contribute) hours upon hours of development time for everything that I have planned.\n\n## Golden Popcorn Performance Index Changes\n\nThe current GPPi algorithm is strong, but fundamentally flawed. It does not take into consideration release groups who have no data. There are terrific new groups (ZoroSenpai for example) who should be tier ~2 at least, but aren't simply because they have no data. How do we fix this?\n\n### Popularity\n\nFor every encode at a specific resolution for a movie / tv show that is currently _popular_, a release group receives +1 score to their GPPi. At the end of every month, the score is reset, and the previous score is normalized (tbd on how) and added to their permanent GPPi score (up to a certain point and probably never past tier ~3)\n\nThis process will be completely automatic and will hopefully solve the problem of new good release groups.\n\n### Grouping\n\nThe previous 'tiers' for release groups was just natural intuitive grouping. Humans are surprisingly very, very good at pattern recognition so it was never really a problem. However, it was manual, and we dont like manual around here. Enter 'K Means Clustering'. Essentially it's just a fancy algorithm that finds natural break points between groups of numbers. Using K means, I've dropped the number of 1080p Tiers from 7 down to 5 which in turn has increased immutability. Small changes, but will be important in the long run.\n\n## Thank You!\n\nThat's all for today, I hope everyone's doing alright and enjoying the holidays :grinning:",
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"last_modified": "2025-01-24T01:55:59.899131+00:00",
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"last_modified": "2025-01-24T02:01:29.977044+00:00",
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"title": "Vision (Almost) Realised",
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"slug": "vision_almost_realised",
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"author": "santiagosayshey",
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{
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"updated_at": "2025-01-24T01:56:02.242357+00:00",
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"updated_at": "2025-01-24T02:01:34.563658+00:00",
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"folders": [
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"_id": "EEi",
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"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 your specific needs**. At 1080p:\n\n- Space-conscious users might prefer **smaller files (5-10% of source)** with quality trade-offs\n- Quality-focused users might push towards **higher quality (30-40% of source)** for transparency\n- Most users find a sweet spot in the middle\n\nHowever, there are technical limits - files larger than **40% for 1080p** and **60% for 2160p** provide no meaningful benefits.\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 **HEVC stops being efficient compared to AVC**. We do this using two key video quality metrics:\n\n- **VMAF** - 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 codecs achieve **near identical quality**\n- At ratios above 40%, **AVC becomes preferred** due to better tooling and 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 **visual transparency** becomes achievable\n- Higher ratios provide **diminishing returns**\n- At this compression level, content achieves **VMAF scores above 95**\n- **Storage efficiency** becomes critical due to larger base file sizes\n- Quality improvements become **increasingly subtle** beyond this point\n\nRead these articles to better understand how VMAF and BD-Rate tell us how efficient a codec is[^1][^2]:\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 compression target when:\n\n- Managing large libraries on limited storage\n- Collecting complete series where total size is a major concern\n- Primarily viewing on mobile devices or smaller screens\n- Dealing with bandwidth caps or slow internet connections\n\n| Tier | Group | Efficiency | Delta |\n| ---- | ----------------------- | ---------- | ----- |\n| 1 | iVy | 9.37% |Line truncated
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"last_modified": "2025-01-24T01:55:59.909131+00:00",
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"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 your specific needs**. At 1080p:\n\n- Space-conscious users might prefer **smaller files (5-10% of source)** with quality trade-offs\n- Quality-focused users might push towards **higher quality (30-40% of source)** for transparency\n- Most users find a sweet spot in the middle\n\nHowever, there are technical limits - files larger than **40% for 1080p** and **60% for 2160p** provide no meaningful benefits.\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 **HEVC stops being efficient compared to AVC**. We do this using two key video quality metrics:\n\n- **VMAF** - 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 codecs achieve **near identical quality**\n- At ratios above 40%, **AVC becomes preferred** due to better tooling and 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 **visual transparency** becomes achievable\n- Higher ratios provide **diminishing returns**\n- At this compression level, content achieves **VMAF scores above 95**\n- **Storage efficiency** becomes critical due to larger base file sizes\n- Quality improvements become **increasingly subtle** beyond this point\n\nRead these articles to better understand how VMAF and BD-Rate tell us how efficient a codec is[^1][^2]:\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 compression target when:\n\n- Managing large libraries on limited storage\n- Collecting complete series where total size is a major concern\n- Primarily viewing on mobile devices or smaller screens\n- Dealing with bandwidth caps or slow internet connections\n\n| Tier | Group | Efficiency | Delta |\n| ---- | ----------------------- | ---------- | ----- |\n| 1 | iVy | 9.37% |Line truncated
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"last_modified": "2025-01-24T02:01:29.986044+00:00",
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"title": "Encode Efficiency Index",
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"slug": "EEi",
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"author": "santiagosayshey",
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{
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"_id": "GPPi",
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"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 $r$",
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"last_modified": "2025-01-24T01:55:59.909131+00:00",
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"last_modified": "2025-01-24T02:01:29.986044+00:00",
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"title": "Golden Popcorn Performance Index",
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"slug": "GPPi",
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"author": "santiagosayshey",
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@@ -32,7 +32,7 @@
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{
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"_id": "home",
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"content": "# \ud83d\udc4b Hey!\n\nWelcome to Dictionarry! This project aims to wiki-fy and **simplify media automation** in Radarr / Sonarr through extensive, data driven documentation, custom formats and quality profiles.\n\n---\n\n## \ud83d\udca1 Motivation\n\nNavigating the world of media automation and coming across quality terms like \"Remux\", or \"HEVC\" or \"Dolby Vision\" can be quite daunting when all you want to do is setup a media server to watch some content. If often **feels like you need a masters in audio / video just to grab the latest blockbuster.** Dictionarry aims not to explain these concepts in detail, but **abstract them into more approachable ideas** that don't require extensive knowledge or experience.\n\nDictionarry leverages two key features of Radarr and Sonarr to simplify media automation:\n\n1. Custom Formats - Think of these as smart filters that scan release titles for specific patterns. They help **identify important characteristics** of your media, such as:\n\n - Video quality (4K, HDR, Dolby Vision)\n - Audio formats (Atmos, DTS, TrueHD)\n - Source types (Remux, Web-DL, Blu-ray)\n - Potential issues (upscaled content, poor encodes)\n\n2. Quality Profiles - These act like a scoring system that **ranks releases** based on their Custom Format matches. You can:\n - Prioritize what matters most to you\n - Automatically upgrade to better versions\n - Avoid problematic releases\n\nThink of Dictionarry as your personal car-buying expert: Instead of researching every technical specification and test-driving dozens of vehicles, you get access to a curated showroom of pre-vetted options that match what you're looking for. Whether you want:\n\n- 2160p Remux - **Maximum Quality** 4K HDR remuxes with lossless audio and Dolby Vision\n- 2160p Quality - **Transparent 4K** HDR encodes selected using the Encode Efficiency Index\n- 1080p Quality - **Transparent 1080p** encodes optimized using the Golden Popcorn Performance Index\n- 1080p Efficient - **Efficient x265 1080p** Encodes optimized to save space using the Encode Efficiency Index\n- And More....\n\n\n\nDictionarry's database of tested profiles and formats handles the technical decisions for you.\n\n---\n\n## \u2699\ufe0f Profilarr\n\nThe database by itself does nothing. Custom Formats and Quality Profiles **need to be imported** and configured in your individual arr installations. Rather than leaving you to manually create everything yourself based on our guides, we've created **Profilarr** to automate this process.\n\nProfilarr is a **configuration management tool** for Radarr and Sonarr that can interface with **ANY remote configuration database** (not just Dictionarry's!). It automatically:\n\n- **Pulls** new updates from your chosen database\n- **Compiles** the database format into specific arr formats\n- **Imports** them to your arr installations\n- Manages version control of your configurations\n\nBuilt on top of git, Profilarr treats your configurations like code, allowing you to:\n\n- Track changes over time\n- Maintain your own customizations while still receiving database updates\n- Resolve conflicts between local / remote changes when they arise\n\nThe architecture was specifically built like this to **put user choice first**. We believe that:\n\n- **Your media setup should reflect your needs, not our opinions**\n- Updates should enhance your configuration, not override it\n- Different users have different requirements (storage constraints, hardware capabilities, quality preferences)\n- The ability to customize should never be sacrificed for convenience\n\nProfilarr empowers you to use Dictionarry's database (or anyone elses!) as a foundation while maintaining the freedom to adapt it to your specific needs.",
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"last_modified": "2025-01-24T01:55:59.909131+00:00",
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"last_modified": "2025-01-24T02:01:29.986044+00:00",
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"title": "home",
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"slug": "home",
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"author": "santiagosayshey",
|
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|
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Reference in new issue
Block a user