H 264 Avc Encoder (amd Advanced Media Framework) Download

You will be able to get x264 to look better than the AMD advanced media framework, but it puts a lot of load on your CPU. I recommend you test the AMD advanced media framework first and see if the quality is good enough at whichever bitrate you can handle. If not, switch to to x264 and play around with the presets. Find one where you can still play the game without lag or dropped frames, but gives the highest quality (slower preset = higher quality video).

GStreamer 1.22 open-source and cross-platform multimedia framework is out today as a major release that brings numerous improvements, new features, and many other changes to provide you with a top-notch multimedia experience.

Download 🔥 https://cinurl.com/2y3Bap 🔥

AMD Advanced Media Framework is a light-weight, portable multimedia framework that abstracts away most of the platform and API-specific details and allows for easy implementation of multimedia applications using a variety of technologies, such as DirectX 11, OpenGL, and OpenCL and facilitates an efficient interoperability between them.

GPUOpen introduced the advancement and the direct source contribution of AMD's Advanced Media Framework (AMF). The purpose of this framework is to enable GPU processing of media encode and decode efforts with minimal impact to the CPU and in general end user. With that said, this is not exclusive to AMD as NVIDIA has it's own GPU enabled media processing support but it's closed source and hasn't been made widely public. Because of AMD's open source nature, it allows individual contributors to make their additions and projects to further the progress of features, improved support and performance widely available.

REST framework includes a number of built in Renderer classes, that allow you to return responses with various media types. There is also support for defining your own custom renderers, which gives you the flexibility to design your own media types.

It's important when specifying the renderer classes for your API to think about what priority you want to assign to each media type. If a client underspecifies the representations it can accept, such as sending an Accept: */* header, or not including an Accept header at all, then REST framework will select the first renderer in the list to use for the response.

A multimedia framework and API produced by Microsoft for software developers to perform various operations with media files. Most Windows video-related applications on Windows, such as Microsoft's Windows Media Player, use DirectShow to manage multimedia content.

This update uses AMD AMF (Advanced Media Framework) multimedia processing technology to speed up video encoding on systems with any AMD GPU, including the AMD Radeon Graphics built into every AMD Ryzen U-Series mobile processor. Yes, you may now even render video on your business class ultrathin notebooks if you wish to. Results from exporting a 4-minute Apple ProRes 4444 4K 60p QuickTime video clip to the Premiere Pro H.264 4K also showed how big of a factor GPU encoder is when it is enabled in Adobe Premiere Pro. Take a look at the results below.

Well in this case, you're going to use an AVAssetReaderOutput and an AVAssetWriterInput. You're responsible for sending samples from one to the other. Let's go over our new AVAssetWriterInput APIs. So like AVAssetExportSession, you need to enable multi-pass, so set this to yes and you're automatically opted in. Then after you're done appending samples, you need to mark the current pass as finished. So what does this do? Well, this triggers the encoder analysis. The encoder needs to decide if I need to perform multiple passes and if so, what time ranges. So the encoder might say, "I want to see the entire sequence again," or "I want to see subsets of the sequence." So how does the encoder talk about what time ranges it wants for the next pass? Well, that's through an AVAssetWriter InputPassDescription. So in this case, we have time from 0 to 3, but not the sample at time 3, and samples from 5 to 7, but not the sample at time 7. So a pass description is the encoder's request for media in the next pass, and it may contain the entire sequence or subsets of the sequence. On a pass description, you can query the time ranges that the encoder has requested by calling sourceTimeRanges.

Inside that callback you call currentPassDescription. This asks the encoder what time ranges it wants for the next pass. If the pass is non-nil (meaning the encoder wants data for another pass) you reconfigure your source. So this is where the source will send samples to the AVAssetWriterInput, and then you prepare the AVAssetWriterInput for the next pass. You're already familiar with requestMediaData WhenReadyOnQueue. If the pass is nil, that means the encoder has finished passes. Then you're done. You can mark your input as finished. All right, let's say you're going from a source media file. That was in our second example. So we have new APIs for AVAssetReaderOutput. You can prepare your source for multi-pass by saying supportsRandomAccess equals yes. Then when the encoder wants new time ranges, you need to reconfigure your AVAssetReaderOutput to deliver those time ranges. So that's resetForReadingTimeRanges with an NSArray of time ranges.

If you're concerned with using the minimum amount of temporary storage during the encode or transcode operation, use single-pass. The encoder analysis storage and the frame database will use more storage than the output media file.

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling. 2351a5e196