Over the last three decades, advances in video coding algorithms have led to ever improving compression rates. With video occupying more than 80% of all IP traffic by 2021, the end of the quest for bitrate saving is not in sight: every network upgrade is quickly diminished by the increasing demand for content. A multitude of different video coding standards are available to tackle this issue, with every codec generation aiming at 30-50% increase in compression efficiency at the expense of increasing complexity. The latter aspect means that new standards are being adopted very slowly: for instance, while MPEG HEVC was standardized in 2013, its high licensing fees and complexity (especially for HD and 4K content) have not yet allowed for wide adoption.
Where does iSize fit in? iSize is the first company to offer proprietary machine learning solutions for substantial bitrate or quality gains in video compression. Beyond its performance, what makes our solution stand out is that it is compatible with any existing video coding infrastructure. Therefore, it can:
- boost the compression efficiency of any video codec;
- run on client devices with minimal or no additional overhead;
- offer significant computational and energy efficiency for video encoding on resource-constrained devices (drones, action-cams, smartphones, etc.).
These advantages allow our clients to seamlessly integrate our solution and benefit from bitrate saving and quality improvement for their video delivery services.
Bitrate Saving Video Examples
20-60% bitrate saving for AVC/H.264 and HEVC in HD/4K
Our Core Innovation
Future coding standards, like the ongoing VCEG/MPEG JVET standardization to create the next generation codec that will replace HEVC, will undertake a lengthy development process that will typically culminate in 30%-40% bitrate saving for the same visual quality. However, the expected timeline for delivery of the first working codecs for MPEG’s JVET current standardization is scheduled for after 2023 – at the same time, the HEVC standard (finalized in 2013) has still not reached large rollout to date. On the other hand, current machine learning solutions like Magic Pony (Twitter) and Wave One offer disruptive performance for still-image coding; however, such solutions face substantial barriers when moved to video due to the unresolved challenge of incorporating temporal prediction and their deployment complexity.
Our bitrate saving and quality improvements are achieved by incorporating iSize’s proprietary downscaling-upscaling technology as a pre- and post-processing stage of a standard codec pipeline. The entire process is shown in Figure 1. Instead of abandoning the existing codec pipeline (as proposed by deep autoencoding solutions, such as Magic Pony/Twitter and Wave One), iSize’s encoder-side solution downscales the input content with a custom-designed low-complex filter. The iSize decoder-side solution upscales the decoded low-resolution video to obtain the final result. Our IP offers 20%-40% rate saving or quality improvement (2-4dB of PSNR) over AVC/H.264 and HEVC at marginal complexity increase for the decoder and complexity reduction for the encoder.
Unlike other machine learning efforts in this space, our solution is deployable today, and can be used on top of any standards-compliant or proprietary video codec architecture with minimal increase in complexity.
Figure 1. iSize technology for bitrate saving over a conventional codec architecture