Neural Stereo Video Compression with Hybrid Disparity Compensation

• URL: http://arxiv.org/abs/2504.20383v1
• Date: Tue, 29 Apr 2025 03:04:09 GMT
• Title: Neural Stereo Video Compression with Hybrid Disparity Compensation
• Authors: Shiyin Jiang, Zhenghao Chen, Minghao Han, Xingyu Zhou, Leheng Zhang, Shuhang Gu,
• Abstract summary: We propose a hybrid disparity compensation (HDC) strategy that leverages explicit pixel displacement as a robust prior feature to simplify optimization.<n>We introduce a novel end-to-end optimized neural stereo video compression framework, which integrates HDC-based modules into key coding operations.
• Score: 21.811261355652356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can be broadly categorized into two types: one that employs explicit horizontal shifting, and another that utilizes an implicit cross-attention mechanism to reduce cross-view disparity redundancy. In this work, we propose a hybrid disparity compensation (HDC) strategy that leverages explicit pixel displacement as a robust prior feature to simplify optimization and perform implicit cross-attention mechanisms for subsequent warping operations, thereby capturing a broader range of disparity information. Specifically, HDC first computes a similarity map by fusing the horizontally shifted cross-view features to capture pixel displacement information. This similarity map is then normalized into an "explicit pixel-wise attention score" to perform the cross-attention mechanism, implicitly aligning features from one view to another. Building upon HDC, we introduce a novel end-to-end optimized neural stereo video compression framework, which integrates HDC-based modules into key coding operations, including cross-view feature extraction and reconstruction (HDC-FER) and cross-view entropy modeling (HDC-EM). Extensive experiments on SVC benchmarks, including KITTI 2012, KITTI 2015, and Nagoya, which cover both autonomous driving and general scenes, demonstrate that our framework outperforms both neural and traditional SVC methodologies.

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