The accelerating shift towards remote work, immersive entertainment, and digital-first education is fueling an insatiable global demand for high-quality, low-latency video. This trend intensifies the need for efficient data management and transmission solutions across industries. Simultaneously, businesses face increasing pressure to optimize infrastructure costs and reduce the environmental footprint of data centers. This technology directly addresses these challenges by enabling significant data compression, supporting both economic efficiency and sustainability objectives in the digital economy.
Maximizes Entropy Reduction: Efficiently reduces residual signal entropy in intra-prediction, achieving up to a 25% data volume reduction compared to conventional encoding.
Optimizes Predictive Images Adaptively: Inverts and orthogonally transforms residual signals based on reference pixel positions, significantly improving prediction accuracy and enabling substantial data compression while maintaining high image quality.
Enhances Performance with Secondary Transforms: Selectively applies optimal secondary orthogonal transforms based on intra-prediction mode and reference pixel position, ensuring peak encoding performance even for complex video content.
This patent provides robust protection for an encoding device, decoding device, and program, covering adaptive orthogonal transformation of residual signals in intra-prediction. Its strong claims, developed with expert legal counsel and successfully overcoming examiner objections, indicate high originality and stability for licensees.
This patent primarily covers intra-prediction optimization. Licensees could explore building additional IP around inter-prediction enhancements, AI-driven content-aware encoding, or novel hardware acceleration architectures for this algorithm.
Assuming a 20% improvement in video data encoding efficiency. For an enterprise handling 10TB of video data monthly, with current annual data transmission and storage costs of ~$65K (AI est.), this technology could yield an annual cost reduction of ~$50K (AI est.). This benefit scales proportionally with data volume, potentially reaching hundreds of thousands of dollars annually for large-scale operations.
X: Maximized Encoding Efficiency
Y: Adaptability and Flexibility