TECHNICAL Streaming high-resolution video comes with an inevitable trade-off between available bandwidth and quality of experience for the end user. Delivering uncompromised video quality typically results in high bitrates, which can lead to slow starts, video buffering and high content delivery network (CDN) and storage costs. Traditional solutions that attempt to minimise bandwidth without compromising quality are centered around the development of more intelligent video encoders.
that preserves quality while reducing file size and bandwidth requirements. These are benefits that are otherwise only achievable through the introduction of newer, more complex compression standards that will entail high cost, long time to market, and compatibility issues within the broader ecosystem. Heuristic CAE solutions have since emerged that dramatically reduce compute requirements while achieving close to optimal results. Jan Ozer has compared various options for H.264 encoding. 6 Additionally, innovative AI methods are now being applied to content transcoding to reduce bitrate and improve the visual experience. AI is used to dynamically analyse frames and scenes to tune encoder modes that impact both bitrate and video quality. Bitrate reduction helps lower cost of transmission and storage for media providers. In encoding, parameter choices are impacted by several factors, such as the type of delivery, including live, video on demand (VoD), and file-to-file (F2F) transfer. Furthermore, media providers use various infrastructures in their data centers for encoding content. These technologies can include specialised software encoders running on CPUs, as well as GPUs with built-in transcoding accelerators. Intel works with the ecosystem that supports these media providers to apply the power of Intel® technologies, such as Intel Xeon processors and the Intel® Media Transcode Accelerator. Intel’s commitment to the ecosystem aims to help lower costs of operations while improving content delivery and thus the viewing customers’ experiences. VisualOn Optimizer suite VisualOn is a streaming solutions provider that offers a universal, encoder-agnostic, content-adaptive encoding technology. The VisualOn Optimizer suite enables streaming media companies to deliver compelling multimedia content with incredible user experience.
VisualOn introduced the Optimizer suite at IBC 2023, 7 and it won the company the Product of the Year award at NAB 2024. 8 Optimizer provides a single-pass transcoding solution that dynamically adjusts encoding settings on a per-frame basis. Supporting AVC, HEVC, and AV1, the Optimizer suite integrates with any encoder running on CPU- or GPU-based hardware. It supports a wide variety of use cases with product versions for the following deployment types: n Optimizer VoD. For VoD workflows, using FFmpeg’s filter complex to transcode the entire adaptive bitrate (ABR) ladder in a single command. n Optimizer Live. For streaming workflows with real-time transcoding. Its efficient implementation allows it to achieve zero additional latency while reducing both average and peak bitrates without compromising visual quality, making it ideal for large events. n Optimizer Fidelity. For visually lossless file-to-file video transcoding to reduce the storage requirements of massive mezzanine video files. n Optimizer. For general purpose file-to- file transcoding to reduce the size of video files. Optimizing media streaming with advanced dynamic coding technology The VisualOn Optimizer uses dynamic encoding technology that combines parameter matching, scene recognition, and image quality enhancement. It leverages a one-pass framework based on machine learning to predict and adjust encoder settings, optimising each video segment based on its features. Based on VisualOn testing, the technology reduces bitrates up to 40 percent,1 while maintaining or improving video quality, as measured by VMAF scores. The technology also provides real- time feedback to dynamically adjust parameters based on scene analysis and quality metrics (PSNR, SSIM, and VMAF). It supports optional preprocessing steps such as sharpening and noise reduction,
Content Adaptive Encoding (CAE) is an innovative encoding process that is revolutionising video streaming by reducing bandwidth usage while enhancing quality. Integrated into streaming media service providers’ workflows, it enables more efficient usage of content delivery systems and reduces storage requirements, without sacrificing user experiences. VisualOn Optimizer is an innovative CAE solution that dramatically reduces average bitrates while enhancing visual quality. It is encoder-agnostic to deliver high-quality content with low bitrates. This white paper describes VisualOn Optimizer and its impact on video encoding performance, compares it against other CAE solutions, and presents test results showing the benefit of combining the Optimizer with the built-in media acceleration of the Intel® Xeon® 6 SoC.
Content Adaptive Encoding
CAE was pioneered by Netflix from 2015 to 2018, with per-title, per-chunk and per-shot encoding. Using their CAE technology, Netflix achieved over 30 percent bitrate reduction 2,3,4 without degrading visual quality, as measured by the Video Multimethod Assessment Fusion (VMAF) score. 5 The approach requires running hundreds of different encodings with different combinations of parameters to select the best results. Such an encoding regimen can be prohibitively expensive and difficult to scale for many companies, especially those with limited budgets or technical capabilities. Building on the strides made by Netflix in encoding strategies, CAE takes video compression a step further by adapting the encoding process to the specific content of each video segment. Unlike traditional encoding methods that apply uniform settings across an entire video, CAE analyses factors such as motion, texture, and complexity within the video to optimise encoding settings dynamically. This results in more efficient compression
Volume 48 No.1 MARCH 2026
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