Residual Entropy Compression for Cloud DVR Storage
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Solution Overview
Problem
Cloud-based DVR services face storage constraints due to legal requirements for each user to have a distinct copy of recorded content, making data deduplication techniques unacceptable, and existing compression schemes inefficient in reducing storage needs.
Innovation Solution
The technique involves vectorizing compressed video streams, applying codebook-based vector quantization to generate residual vectors, removing zero elements, and entropy coding these residuals, with metadata optimization to achieve lossless compression, allowing for efficient storage and transmission while meeting the one copy per user requirement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data deduplication techniques are used to save storage space, then storage efficiency is improved, but legal compliance deteriorates because each user requires a distinct copy of recorded content
Solution Approach 1:
The patent segments video content into discrete blocks and processes each block independently through vector quantization and entropy coding. This segmentation allows individual blocks to be compressed and stored as distinct copies for each user while maintaining the ability to legally comply with one-copy-per-user requirements for the complete content.
Solution Approach 2:
The patent applies parameter changes by transforming video blocks through vector quantization (changing representation parameters) and entropy coding (changing compression parameters). These parameter transformations enable significant storage space reduction while maintaining the distinct copy requirement for legal compliance.
2Speed
If standard compression schemes are used, then transmission speed is improved, but storage efficiency deteriorates due to inability to apply deduplication
Solution Approach 1:
The patent maintains continuous compression through entropy coding of residual blocks, ensuring that the compression process operates continuously on all video content. This continuous compression action achieves both transmission speed improvement and storage efficiency by eliminating redundancy in each user's distinct copy.
Solution Approach 2:
The patent applies parameter changes through vector quantization and entropy coding transformations that optimize both transmission and storage. By changing the representation parameters of video blocks, the system achieves efficient compression suitable for both fast transmission and space-efficient storage of distinct user copies.
3Reliability
If lossless compression is used to maintain content accuracy, then data integrity is improved, but compression ratio deteriorates compared to lossy compression
Solution Approach 1:
The patent substitutes traditional mechanical compression approaches with entropy coding based on statistical properties of residual blocks. This substitution enables lossless compression by replacing direct bit representation with optimized statistical encoding, achieving better compression ratios while maintaining complete data integrity for legal compliance.
Solution Approach 2:
The patent applies parameter changes through entropy coding that transforms residual block parameters into optimized representations. This parameter transformation achieves lossless compression by preserving all original information while reducing the number of bits required, improving compression ratio without sacrificing data integrity.
Data Source
AI summary
Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.