18358094. FLEXIBLE COEFFICIENT CODING IN VIDEO COMPRESSION simplified abstract (Apple Inc.)

FLEXIBLE COEFFICIENT CODING IN VIDEO COMPRESSION

Organization Name

Apple Inc.

Inventor(s)

Alican Nalci of Cupertino CA (US)

Yunfei Zheng of Santa Clara CA (US)

Hilmi Enes Egilmez of Santa Clara CA (US)

Yeqing Wu of Cupertino CA (US)

Yixin Du of Milpitas CA (US)

Alexandros Tourapis of Los Gatos CA (US)

Jun Xin of San Jose CA (US)

Hsi-Jung Wu of San Jose CA (US)

Arash Vosoughi of Cupertino CA (US)

Dzung T. Hoang of San Jose CA (US)

FLEXIBLE COEFFICIENT CODING IN VIDEO COMPRESSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18358094 titled 'FLEXIBLE COEFFICIENT CODING IN VIDEO COMPRESSION

Simplified Explanation

The abstract of the patent application describes a flexible coefficient coding (FCC) approach. This approach involves organizing coefficient samples within a transform unit (TU) or a prediction unit (PU) into variable coefficient groups (VCGs) based on spatial sub-regions. The shape and boundaries of the VCGs can be irregular and determined by the relative distance of coefficient samples or scan ordering within a TU. Each VCG can encode a different number of symbols for a given syntax element or a different number of syntax elements within the same TU or PU.

The innovation of this patent lies in the ability to adapt the coding of symbols or syntax elements based on the type of arithmetic coding engine used. For multi-symbol arithmetic coding (MS-AC), a VCG can encode a different number of symbols for a syntax element. For binary arithmetic coders (BACs), FCC allows for coding a variable number of syntax elements in different VCGs.

Potential applications of this technology include video coding, image compression, and data transmission. The flexible coefficient coding approach can improve the efficiency of encoding and decoding processes in these applications.

This technology solves the problem of efficiently encoding and decoding coefficient samples within a TU or PU. By organizing the samples into VCGs and allowing for variable coding of symbols or syntax elements, the approach provides better granularity for entropy modeling and improves compression efficiency.

The benefits of this technology include improved compression efficiency, reduced bit rate, and enhanced flexibility in adapting to different coding specifications. The use of different context models and entropy coders for each VCG allows for optimized entropy coding, leading to better compression performance.

Original Abstract Submitted

A flexible coefficient coding (FCC) approach is presented. In the first aspect, spatial sub-regions are defined over a transform unit (TU) or a prediction unit (PU). These sub-regions organize the coefficient samples residing inside a TU or a PU into variable coefficient groups (VCGs). Each VCG corresponds to a sub-region inside a larger TU or PU. The shape of VCGs or the boundaries between different VCGs may be irregular, determined based on the relative distance of coefficient samples with respect to each other. Alternatively, the VCG regions may be defined according to scan ordering within a TU. Each VCG can encode a 1) different number of symbols for a given syntax element, or a 2) different number of syntax elements within the same TU or PU. Whether to code more symbols or more syntax elements may depend on the type of arithmetic coding engine used in a particular coding specification. For multi-symbol arithmetic coding (MS-AC), a VCG may encode a different number of symbols for a syntax element. For example, to encode absolute coefficient values inside a TU after performing a transform such as the discrete cosine transform (DCT), a VCG region may be defined around lower-frequency transform coefficients and for that VCG M-symbols can be encoded the absolute coefficient values. Another VCG region can be defined around the higher-frequency transform coefficients to encode K-symbols, where K may be different than M. For binary arithmetic coders (BACs), FCC allows for coding a variable number of syntax elements in different VCGs. In this case, one VCG in a TU may code M-syntax elements associated with signaling the absolute coefficient value, where each one of the M-syntax elements may have 2-symbols. Probability models and context derivation rules may be tailored for each VCG in a given TU or PU. Since each VCG may code a different number of symbols or syntax elements in different spatial locations of a TU or PU, different context models may be used for each VCG to provide better granularity for entropy modeling for arithmetic coding. Furthermore, different VCGs may also use different entropy coders including combinations of arithmetic coding, Golomb-Rice coding, Huffman coding.