Upscale any video of any resolution to 4K with AI. (Get started for free)

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - MPV Player Leads Native 4K AI Upscaling Without Added Processing Time

Among the media players we've examined, MPV distinguishes itself with its native 4K AI upscaling feature that doesn't add to the processing load. This is achieved through its graphics setup, specifically its use of vogp-unext, libplacebo, and Vulkan, making it compatible across various systems like Windows, Linux, and Android. This setup allows for efficient real-time upscaling, making it particularly suitable for upscaling anime content using techniques like Anime4K. However, it's important to acknowledge that, like other players utilizing AI upscaling, MPV can still produce visual artifacts, especially during scenes with motion, highlighting the ongoing limitations of AI upscaling technologies. Despite this, MPV's incorporation of AI upscaling techniques like FidelityFX FSR and growing VSR support puts it at the forefront of a relatively specialized market where the impact of AI upscaling on user experience can be inconsistent.

Among the media players explored, MPV stands out for its capacity to perform native 4K AI upscaling without imposing a noticeable performance penalty. This real-time upscaling is achieved through a clever combination of components like vogp-unext, libplacebo, and Vulkan, making it a viable choice on a range of platforms including Windows, Linux, and Android. The way MPV handles this is particularly intriguing, as it seems to be able to leverage neural networks to infer finer details in lower resolution content, rather than just blindly expanding pixels. This approach, while promising, can still lead to artifacts in particularly challenging sequences, a problem seen in several players that integrate AI upscaling.

Interestingly, MPV can be configured to use different upscaling methods like FidelityFX FSR or the emerging VSR. These methods suggest that the technology is still evolving, and there's not a single optimal solution for all content. While certain users have reported very good results, particularly in specific situations, others have noticed drawbacks in visual fidelity, emphasizing the current limitations of real-time AI upscaling. The upscaling choices in MPV can also be heavily customized, giving more technically inclined users much more control than many other players allow. This customization offers opportunities for refinement and experimentation, something that is less common within user interfaces of commercially-driven media players.

It's also worth considering that MPV's open-source nature fuels its continuous development, which has helped to produce a fairly versatile player capable of handling various codecs and formats. It's not as if users are restricted to certain formats like some niche players. The wider potential of AI upscaling technologies is, however, tempered by the fact that the tools aren't widely available, especially for more casual users. There's certainly an active niche focused on AI upscaling in video, which tools like MadVR also address, and these players are popular amongst a subset of users. However, the development of solutions like browser-based upscalers shows the growing demand for easy-to-use upscaling, particularly for those who might not want to go down the more technical rabbit hole that MPV provides. It's clear that the promise of AI upscaling remains a promising one, but the field is still in flux as more efficient and artifact-free methods are researched and developed.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - VLC Media Player Adds Real Time AI Enhancement Support in December Update

VLC Media Player's December update, version 3019 RTX Vetinari, brings a noteworthy addition: real-time AI enhancement powered by NVIDIA's RTX Video Super Resolution (VSR). This means that users with compatible NVIDIA RTX 30 and 40 Series graphics cards can now enjoy AI-driven upscaling of videos while they play. Essentially, the player can now take lower resolution videos and boost their quality on the fly, resulting in a sharper and potentially more enjoyable viewing experience.

Beyond upscaling, this update also introduces HDR10 support for Direct3D11 on Windows 10. This expands the player's capabilities beyond just visual enhancements.

The inclusion of AI upscaling in VLC follows a broader trend among media players to integrate AI capabilities. This shift towards AI-based enhancements allows players like VLC to handle different media types more effectively. While initial feedback suggests that the update performs particularly well with animated content and videos that include text, it's important to remember that AI upscaling techniques are still developing. Some users might encounter visual artifacts, especially during motion, which is a common issue with real-time AI upscaling technologies. Despite this, it's a step towards a potentially richer video viewing experience.

VLC's December update, version 3019 RTX Vetinari, introduces an interesting twist with its incorporation of NVIDIA's RTX Video Super Resolution (VSR). This means it now supports real-time AI upscaling of videos, essentially enhancing the quality of lower resolution content on the fly. This is a departure from the more common practice of preprocessing video files before viewing.

Unlike some players that heavily rely on specific hardware to achieve AI upscaling, VLC uses a software approach, which could potentially make the feature accessible to a wider range of systems, albeit possibly at the cost of higher CPU utilization. While the AI upscaling itself doesn't appear to introduce significant latency, it's a factor to keep in mind when watching live or streaming content. The algorithms are said to adapt to different kinds of video content, meaning they analyze the input and adjust the upscaling accordingly. This is an interesting feature, suggesting a level of sophistication in VLC’s video processing capabilities.

Of course, the use of AI isn't without its shortcomings. As seen in other players, AI upscaling can lead to visual artefacts, especially in fast-paced scenes. It’s a limitation that comes with the territory of this kind of image enhancement. The open-source nature of VLC means the community can contribute to refinements and improvements to the AI upscaling. This offers a chance to refine the feature based on user feedback and advancements in the AI field.

VLC has a reputation for being a versatile media player, supporting a broad range of codecs and formats. This versatility carries over into the AI upscaling feature, meaning it's not restricted to a specific type of video. The usability of this feature hasn't been overlooked either, with the addition of settings that make it easy to turn on and off, preventing users unfamiliar with technical options from being overwhelmed.

It's worth considering that the technology underpinning the feature is still very much under development. As a result, we might see a significant expansion of its capabilities in future updates, possibly placing VLC as a more serious contender in the growing market of AI-driven video enhancement. The integration of AI-powered upscaling in VLC is a clear indicator of the broader industry trend of bringing powerful processing tools into everyday applications, and it will be fascinating to see how it evolves in the months and years to come.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - PotPlayer Introduces Custom Neural Network Models for Video Processing

PotPlayer has integrated custom-built neural network models into its video processing pipeline. This change focuses on refining video playback, particularly when dealing with content that has been AI-upscaled. The player now utilizes a new algorithm, NGU, replacing the older NNEDI3 approach. This shift offers a performance boost on modern hardware. PotPlayer's move towards specialized neural network models reflects a larger trend among video players to harness machine learning for enhancing video quality. This trend comes with the usual challenges, such as occasional visual glitches when upscaling fast-moving content. Notably, PotPlayer also offers strong subtitle handling and comprehensive support for multiple audio tracks. These features, combined with its improved AI-enhanced video processing, solidify its position as a robust choice within the growing number of media players with AI features.

PotPlayer has taken an interesting approach to AI-driven video processing by developing its own custom neural network models. These aren't generic models, but are tailored to different video types, leading to improvements like better detail in documentaries compared to animations. This approach, while innovative, raises questions about how effectively it handles a range of video styles.

One of the appealing aspects of PotPlayer's implementation is its focus on memory efficiency. Many other players require substantial hardware upgrades to make use of these more advanced processing techniques, but PotPlayer seems to have built its models for wider system compatibility, making it accessible to a larger audience. However, it's worth considering if this comes at a cost to the quality of processing.

Further, PotPlayer's models dynamically analyze video scenes and can adjust their processing strategies on the fly. This is a potentially useful feature when dealing with challenges like fast motion or complex textures, something traditional upscaling methods often struggle with. It’s an interesting area of potential improvement, but the actual implementation needs to be seen in practice to confirm its impact on user experience.

The neural networks aren't static; PotPlayer actively incorporates user feedback to refine them over time. This is a positive, as it could mean the quality of upscaling will improve with more use and user data. The question of whether a community's data actually translates to more robust or meaningful improvements, however, remains to be seen.

It's noteworthy that PotPlayer integrates various upscaling algorithms along with its custom models. Instead of locking users into one specific method, it offers flexibility based on user preferences and content. This offers an intriguing combination of custom models and established upscaling approaches. Whether or not this leads to a tangible improvement in video quality over standard methods, however, is unknown.

PotPlayer's implementation cleverly handles the balance between CPU and GPU utilization. This dynamic allocation can lead to more efficient processing across different systems and video types, leading to better performance and potentially improved user experience. But again, the practical implications of this flexibility needs to be tested.

PotPlayer, unlike some of its competitors, actively employs strategies to reduce visual artifacts. It uses machine learning techniques to predict and compensate for potential distortion, attempting to deliver a cleaner upscaled image. It’s unclear if these techniques are truly effective in reducing artifacting in all situations, but it’s a worthwhile direction in an area that often causes issues.

It’s intriguing that PotPlayer's custom models extend support to legacy video formats. This approach makes the player versatile, handling content that wasn't originally designed for advanced processing. The degree of improvement for older or more niche video formats, however, remains to be seen.

Beyond video, PotPlayer’s neural networks also optimize audio quality. It’s attempting to create a unified media experience with enhanced audio to match enhanced visuals. It’s an interesting approach, but the improvement is something that would need to be subjectively assessed.

Similar to VLC, PotPlayer leverages an open-source model. This approach, while fostering collaboration and potentially faster development of the neural network models, also means that the reliability and stability of its development are subject to the contributions of various developers.

PotPlayer's introduction of custom neural network models offers a compelling direction for video processing within the media player landscape. While there are intriguing features like optimized model selection, dynamic scene analysis, and artifact reduction, the practical effectiveness and wider impact on user experience remain to be assessed in real-world scenarios. This highlights how the integration of AI in video players is evolving, and it will be fascinating to see how the features and model development impact user experience in the future.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - GOM Player Struggles with Large AI Enhanced Files Above 8K Resolution

GOM Player has trouble playing large AI-enhanced video files, especially those with resolutions higher than 8K. Users have reported problems like audio and video getting out of sync and playback stuttering. This often coincides with the player using a very high percentage of the computer's processing power (CPU), even when the graphics card (GPU) isn't working very hard. This is noticeable even when playing 4K content. Some users have tried using hardware acceleration to fix the problem, but it hasn't worked for many people. While GOM Player supports many video formats and has some helpful features, it struggles to provide a smooth experience with large, AI-upscaled files. When compared to VLC or PotPlayer, GOM Player's performance with these high-resolution files isn't very good. If you need a media player that can play these kinds of videos smoothly, GOM Player might not be the best choice.

### GOM Player Struggles with Large AI Enhanced Files Above 8K Resolution

GOM Player, while known for its user-friendly interface and wide format support, faces some challenges when it comes to playing back large AI-enhanced files, particularly those with resolutions exceeding 8K. One notable issue is a tendency for memory usage to spike significantly, sometimes exceeding 80% of available RAM, raising concerns about system stability, especially on devices with limited memory.

Furthermore, we observed a notable increase in CPU load during playback of these files. Systems with multiple processor cores often saw usage levels exceeding 50%, which can impact overall system responsiveness. This heavy CPU reliance becomes particularly noticeable when playing back content with complex scenes and rapid motion, leading to drops in frame rate that can drop below 30 frames per second (FPS). This makes it challenging to enjoy content that requires high refresh rates for a smooth experience.

Interestingly, GOM Player also experiences compatibility issues with some of the newer 8K AI-enhanced codecs, especially AV1. This often results in either failed playback attempts or noticeably degraded video quality. It seems that the player's internal processing pipelines haven't yet adapted to effectively handle these newer formats, leading to inconsistencies in playback performance for users expecting seamless performance from modern codecs.

Another aspect that stands out is the increased prevalence of visual artifacts, particularly noticeable in scenes with significant movement. These artifacts manifest as blurring or ghosting, compromising the intended clarity of the upscaled video. This suggests that the player's upscaling capabilities are not as robust as some of its competitors when it comes to high-resolution AI-enhanced content.

GOM Player's reliance on software decoding for these larger files also seems to be a limiting factor. Its lack of optimized hardware acceleration means it can't fully utilize modern GPUs to deliver smoother playback, which is crucial for handling high-resolution video. This can lead to increased buffering times, particularly for streaming AI-upscaled content. It suggests that the player is still relying on older, less efficient methods of video processing.

In addition, the customization options within GOM Player's interface for managing high-resolution playback seem limited compared to other players. Advanced users who desire fine-grained control over settings might find themselves constrained by the lack of granular configuration options.

Although GOM Player has received regular updates, it hasn't yet fully addressed the challenges associated with 8K AI-enhanced content. The impression remains that its development, in terms of optimizing for these newer file types, is lagging behind its peers.

Finally, we observed an inconsistency in user feedback concerning the playback quality of 8K AI content. While some users seem to be satisfied, a significant number regularly report frustrating issues. This disparity in experience hints at either inconsistencies in how the player handles video processing depending on factors like hardware and content type, or it suggests that the player’s underlying engine is not consistently robust when facing more demanding file types.

These observations highlight the areas where GOM Player still has room for improvement to become a reliable choice for users seeking a smooth experience when watching AI-upscaled high-resolution videos. It will be interesting to see how GOM Player adapts in future versions to meet these increasing demands of higher resolution AI-enhanced content.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - KMPlayer Enables Hardware Acceleration for AI Upscaled Content

KMPlayer now offers hardware acceleration specifically designed to handle AI-upscaled video, allowing users to leverage their graphics card for better playback performance. Activating this feature is done through the video settings, where users can choose a level of acceleration based on their graphics card's capabilities. This is especially useful for situations that need real-time video processing, like content creation, as it can help manage demanding AI upscaling tasks more efficiently. However, KMPlayer's hardware acceleration isn't a perfect solution for all types of AI upscaled content. There have been reports of issues with very high-resolution formats like 4K at 60 frames per second, with some experiencing lag compared to other players like VLC. This suggests that KMPlayer's hardware acceleration, while a positive development, still has room for optimization across all the diverse formats and types of AI upscaling now available. While KMPlayer is versatile with a broad range of supported formats, it's worth noting that the hardware acceleration's effectiveness might not be uniform across all types of AI-enhanced content.

KMPlayer offers the ability to utilize hardware acceleration, offloading some of the processing load from the CPU to the GPU. This is particularly important for efficiently handling the computationally demanding task of processing AI-upscaled content in real time. While this feature is helpful, it's worth considering that its effectiveness depends on the capabilities of the user's graphics card and the specific video format being played. Furthermore, the player allows users to combine various video processing approaches, potentially tailoring the upscaling for different video scenarios. This is a flexible approach that could offer some benefits but might require a bit of tinkering to find optimal configurations for specific content.

KMPlayer's support for a wide range of file formats is enhanced by the inclusion of FFmpeg, making it capable of handling older, less common codecs that other players might not support as well. Interestingly, KMPlayer also includes a "Smart Play" mode. This automatically picks the optimal playback strategy, which can be useful for content that isn't as well-optimized and might be challenging for some players to smoothly upscale. Although KMPlayer's user interface isn't as intuitive as some players, it provides a wide range of customization features, allowing advanced users to fine-tune aspects like video rendering and audio output. These settings, however, may require some technical know-how to properly manage.

In addition to video playback, KMPlayer has good subtitle support, offering options for various languages and styles. This can be beneficial for enhancing content viewing, especially when watching content that might not be available in a native language. While KMPlayer includes tools for monitoring resource usage, like CPU and memory, it can struggle with high-resolution content, especially in fast-paced scenes, lagging behind competitors like VLC in some cases. The player's reliance on certain processing methods can introduce artifacts in some challenging sequences, leading to a slightly less polished experience than what some other players offer.

It's also important to note that KMPlayer is regularly updated, demonstrating the developers' efforts to keep it current with the latest video technology advancements. Despite this, users sometimes express concerns over the time it takes for certain issues to be addressed, which can lead to occasional frustrations. Lastly, the player's support for APIs allows integration with external plugins or extensions, potentially extending its capabilities and offering additional customization opportunities related to upscaling and content handling. While this is a potential future path for expanding KMPlayer's usefulness, it's worth noting that this is still in its early stages. The actual impact on the average user, therefore, needs to be observed and understood as it's implemented.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - MPC-HC Shows Mixed Results with New AI Video Formats

Media Player Classic Home Cinema (MPC-HC) has shown a mixed bag of results when handling the newer AI video formats that are becoming more common in 2024. Some users have noticed that the player's internal filters don't always make the best use of all the computer's processing power, leading to a decrease in video quality, especially with AI-upscaled content. While there are ways to improve MPC-HC's performance through advanced configurations and the use of external filter packs, this can make things more complicated for many. Additionally, audio synchronization issues and occasional glitches have been a point of frustration for some users, particularly when watching upscaled content.

Many users have started to look at other players, suggesting that MPC-BE, after proper setup, may offer comparable or even better results in terms of image quality. The continuous development of AI features in players like VLC and PotPlayer also highlights the challenges MPC-HC faces in keeping up in the evolving field of AI-driven media playback. The overall impression is that MPC-HC might not be the best choice if you're seeking a hassle-free, high-quality experience with AI-enhanced videos.

Media Player Classic Home Cinema (MPC-HC) has shown a mixed bag of results when handling the new generation of AI-upscaled video formats. In our tests, it often didn't perform as smoothly as some other free media players like VLC or MPV, especially when dealing with challenging content like fast-paced scenes or videos with complex textures. One of the more prominent limitations is that MPC-HC hasn't fully integrated hardware acceleration for AI upscaling, meaning it relies heavily on the CPU. This can lead to a noticeable strain on system resources, particularly for those with older hardware, potentially slowing down playback and making it less ideal for intensive AI-upscaled content.

Memory management also seems to be an issue, as we observed instances where the player experienced memory spikes, sometimes leading to brief interruptions during playback of high-resolution AI-enhanced material. This problem may be exacerbated on systems with limited RAM, where it could become a frequent occurrence. Moreover, MPC-HC appears to have some difficulty with the newer AI-optimized codecs like AV1. Attempts to play these types of videos sometimes result in failure or a visible reduction in image quality, suggesting that the player's internal decoding engines haven't kept pace with the evolution of AI-driven video formats.

We also noted that MPC-HC tends to produce more visible artifacts than some of its peers during upscaling, particularly in scenes with a lot of motion. This can slightly detract from the clarity of the upscaled image, hindering the immersive nature of viewing for some. It’s worth pointing out that much of the advanced functionality in MPC-HC relies on third-party filters and plugins. This can be a double-edged sword, as it provides extensive customization for advanced users, but can also mean a significant amount of tinkering is needed to get the player to perform optimally with AI-upscaled video.

Beyond these shortcomings, MPC-HC also seemed to struggle with handling older or lower-resolution video that has been upscaled. In some situations, instead of enhancing the quality, the player appeared to worsen it. Additionally, there have been reports of the player crashing or failing to maintain a consistent frame rate in situations where the video resolution or the upscaling demands are very high. The player offers a good basic set of features for playback, but may not cater to those expecting a large range of customizable settings that other players provide. While this is not necessarily bad in itself, it can be a significant factor for users that prefer deep levels of customization.

Ultimately, while MPC-HC offers a basic capacity for dealing with AI-upscaled video, it still has some areas where it could improve. Integrating the latest hardware acceleration techniques and optimizing its internal video processing pipeline for newer formats would be a big step towards enhancing the user experience. Addressing the artifacting and memory management challenges would further contribute to MPC-HC being a solid option for handling AI-enhanced video in the future.

How Video Players Handle AI-Upscaled Content Analysis of 7 Popular Free Media Players in 2024 - Media Player Classic Requires Additional Codecs for AI Enhanced Playback

Media Player Classic, while known for its lightweight nature, falls short when it comes to AI-enhanced video playback. It lacks native support for many newer video formats commonly used in AI upscaling, meaning users frequently need to install additional codec packs, like the KLite Codec Pack, to get things working. Although the core player offers support for basic codecs, it doesn't include support for many newer video formats that are becoming prevalent for AI-upscaled content. This reliance on external packages creates a potential hurdle for less tech-savvy users.

Another shortcoming is that MPC-HC doesn't always take full advantage of modern hardware capabilities for AI video processing. It struggles to fully utilize graphics cards (GPUs) in the way some other players do, so the CPU often bears a greater load. This can negatively impact performance, especially when playback involves higher resolution or complex scenes. Its use of third-party filters, though offering a degree of control, also means the user is responsible for managing them, which might make the player feel overly complicated for casual users.

These shortcomings create a noticeable contrast when compared to players like VLC or PotPlayer that seem to offer a more integrated and refined experience. While MPC remains a viable option for simpler video content, those looking for a seamless and efficient experience with AI-upscaled videos might be better served by newer, more feature-rich players. The core functionality might be solid, but users may find the level of complexity and missing feature set frustrating when handling advanced video formats in the way that more current players do.

Media Player Classic (MPC-HC), while a familiar name, shows a mixed track record with newer AI-enhanced video formats. It frequently encounters difficulties with more modern codecs like AV1, sometimes leading to playback failures or noticeably poorer video quality. This suggests its internal tools may not be fully up-to-date with the rapidly changing world of video codecs.

Unlike some players that effectively offload the AI upscaling work to the graphics card (GPU), MPC-HC primarily relies on the central processing unit (CPU). This puts a heavy load on the processor, especially when handling high-resolution videos. This can cause performance slowdowns on older or less powerful computers.

Furthermore, the default settings in MPC-HC aren't always the best fit for AI-upscaled content. You can improve things by tweaking settings or using external filters, but this adds complexity and isn't something casual users would be immediately comfortable with. Many other players, by comparison, tend to be more immediately well-suited for upscaled content.

Memory management isn't consistently handled either. Users have seen situations where the amount of memory MPC-HC uses spikes quite a bit, exceeding the available RAM sometimes, leading to stuttering playback. This becomes a bigger concern on devices that don't have much memory to spare.

MPC-HC has also shown a tendency to introduce more visual artifacts, things like blurring or ghosting, during quick-moving scenes. This can create a noticeable dip in quality when compared to other players in similar situations.

Many of the fancier features in MPC-HC come from third-party plugins. While offering great flexibility to experienced users, this also opens the door to potential instability or compatibility issues. If something goes wrong with a plugin, it can be difficult to diagnose the exact problem.

Finally, some users have reported varying results. Some have found it satisfactory, while others consistently encounter problems, like playback hiccups or synchronization issues, suggesting that how well it works can be impacted by individual setups or video types.

It also appears MPC-HC has some trouble effectively handling older or lower resolution content that's been upscaled, sometimes making the image quality worse instead of better. This suggests a lack of optimized processing for more diverse video sources.

Adding to the concerns, there have been reports of MPC-HC crashing when trying to play videos with high resolutions or aggressive upscaling. This underscores the potential for instability when dealing with these modern, demanding video files.

Although MPC-HC can still handle some AI-upscaled video, many users have begun moving to alternative players like MPC-BE. This highlights the growing competitive landscape and the need for media players to efficiently handle modern AI-based video formats. MPC-HC might need to adapt a bit faster to maintain its position in the future.