A Quantitative Approach To The Temporal Dependency in Video Coding
원문 초록
Motion compensated prediction is central to the efficiency of video compression. Its predictive coding scheme propagates the quantization distortion through the prediction chain and creates a temporal dependency. Prior research typically models the distortion propagation based on the similarity between original pixels under the assumption of high resolution quantization. Its efficacy in the low to medium bit-rate range, where the quantization step size is largely comparable to the magnitude of the residual signals, is questionable. This work proposes a quantitative approach to estimating the temporal dependency. It evaluates the rate and distortion for each coding block using the original and the reconstructed motion compensation reference blocks, respectively. Their difference effectively measures how the quantization error in the reference block impacts the coding efficiency of the current block. A recursive update process is formulated to track such dependency through a group of pictures. The proposed scheme quantifies the temporal dependency more accurately across a wide range of operating bit-rates, which translates into considerable coding performance gains over the existing contenders as demonstrated in the experiments.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Compress Any Segment Anything Model (SAM)
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization
any4: Learned 4-bit Numeric Representation for LLMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.