The proliferation of 5G networks and the increasing demand for immersive digital experiences are fueling an explosion in high-resolution, multi-view video content. Industries from entertainment to healthcare are grappling with the immense data transfer and storage costs associated with these rich media formats. This technology offers a critical solution, enabling companies to meet rising consumer expectations for quality and immersion while managing infrastructure costs and enhancing service delivery.
Achieves High Compression with Perceptual Quality Retention: Prioritizes image quality in depth-sensitive regions while efficiently compressing other areas, potentially reducing overall data volume by up to 20% while maintaining high visual fidelity.
Optimized for Multi-View Video: Efficiently encodes multi-view images displayed on light field displays, reducing data transfer load without compromising immersion in VR/AR content and metaverse environments.
Establishes High Technical Uniqueness: With only two prior art documents cited, this technology demonstrates strong originality, making it difficult for examiners to identify similar techniques. This could enable licensees to establish a clear market advantage and secure early market share.
This patent comprehensively protects an image filtering apparatus, its program, an image encoding apparatus, and its program, all based on depth information. With only two prior art documents cited, the patent successfully navigated a rigorous examination process, indicating strong originality and clear claim scope. This establishes a robust and stable right with low invalidation risk from competitors.
This patent primarily covers depth-aware image filtering and encoding algorithms. It leaves white space for innovations in adaptive streaming protocols, real-time hardware acceleration for specific chipsets, or advanced content delivery network (CDN) integrations.
Assuming a 15% average improvement in video content compression efficiency, a company streaming 10TB of video data monthly could reduce annual data transfer by approximately 18TB. With cloud data transfer costs at ~$13.50/TB (AI est.), this translates to an annual direct cost saving of ~$250 (AI est.). For large-scale video streaming services, annual cost reductions could range from ~$0.5M to ~$5M (AI est.).
X: Data Efficiency
Y: Visual Quality Retention