Efficient in-situ image and video compression through probabilistic image representation
Efficient in-situ image and video compression through probabilistic image representation
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通过概率图像表示实现高效的原位图像和视频压缩
DOI：
10.1016/j.sigpro.2023.109268
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发表时间：
2024
影响因子：
4.4
通讯作者：
Ma, Li
中科院分区：
文献类型：
--
作者：
Liu, Rongjie;Li, Meng;Ma, Li
快速有效的多维图像压缩对于高效存储和传输大量高分辨率图像和视频变得越来越重要。在本文中，我们提出了一种有效的多维图像和视频压缩的原位方法，称为自适应递归分割压缩（CARP）。CARP在图像的递归分区上使用贝叶斯概率模型推断出的图像像素的最佳排列，以降低其有效维数，实现保留信息的简约表示。采用多层贝叶斯分层模型实现原位压缩，并进行自调整和正则化，用户只需指定一个参数即可达到期望的压缩率。我们提出的方法的特性包括在大范围压缩率下的高重建质量，同时保留关键的局部细节，适用于各种不同的图像/视频类型和不同的维度，计算可扩展性，渐进传输和易调优。使用各种数据集（包括2D静态图像、真实YouTube视频和监控视频）进行的大量数值实验表明，CARP与广泛流行的图像/视频压缩方法（包括JPEG、JPEG2000、AVI、BPG、MPEG4、HEVC、AV1和三种基于神经网络的方法）相比具有优势，并且通常都优于后者。
Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi-dimensional image and video compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. Furthermore, it adopts a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. The properties of our proposed method include high reconstruction quality at a wide range of compression rates while preserving key local details, applicability to a variety of different image/video types and of different dimensions, computational scalability, progressive transmission and ease of tuning. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP compares favorably to—and often uniformly outperforms—a wide range of popular image/video compression approaches, including JPEG, JPEG2000, AVI, BPG, MPEG4, HEVC, AV1, and three neural network-based methods.