Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf
feature generators and have been shown to perform very well on a variety of
tasks. Unfortunately, the generated features are high dimensional and expensive
to store: potentially hundreds of thousands of floats per example when
processing videos. Traditional entropy based lossless compression methods are
of little help as they do not yield desired level of compression, while general
purpose lossy compression methods based on energy compaction (e.g. PCA followed
by quantization and entropy coding) are sub-optimal, as they are not tuned to
task specific objective. We propose a learned method that jointly optimizes for
compressibility along with the task objective for learning the features. The
plug-in nature of our method makes it straight-forward to integrate with any
target objective and trade-off against compressibility. We present results on
multiple benchmarks and demonstrate that our method produces features that are
an order of magnitude more compressible, while having a regularization effect
that leads to a consistent improvement in accuracy.

为了解决传统基于信息熵的无损压缩方法在降维时效果不佳的问题，本文提出了一种基于学习的方法，可以同时优化特征的可压缩性和任务目标，从而生产一个更具压缩性的特征，同时提高其准确性。

可压缩特征的端到端学习

End-to-end Learning of Compressible Features

Compression has been an important research topic for many decades, to produce
a significant impact on data transmission and storage. Recent advances have
shown a great potential of learning image and video compression. Inspired from
related works, in this paper, we present an image compression architecture
using a convolutional autoencoder, and then generalize image compression to
video compression, by adding an interpolation loop into both encoder and
decoder sides. Our basic idea is to realize spatial-temporal energy compaction
in learning image and video compression. Thereby, we propose to add a spatial
energy compaction-based penalty into loss function, to achieve higher image
compression performance. Furthermore, based on temporal energy distribution, we
propose to select the number of frames in one interpolation loop, adapting to
the motion characteristics of video contents. Experimental results demonstrate
that our proposed image compression outperforms the latest image compression
standard with MS-SSIM quality metric, and provides higher performance compared
with state-of-the-art learning compression methods at high bit rates, which
benefits from our spatial energy compaction approach. Meanwhile, our proposed
video compression approach with temporal energy compaction can significantly
outperform MPEG-4 and is competitive with commonly used H.264. Both our image
and video compression can produce more visually pleasant results than
traditional standards.

本文提出了一种使用卷积自编码器进行图像压缩并将其推广至视频压缩的架构，通过在编码器和解码器的两个端口添加插值循环，将空时能量压缩实现到了图像和视频压缩上。作者还提出了一种在损失函数中添加基于空间能量压缩的惩罚项的方法，以实现更高的图像压缩性能，同时针对视频内容的动态特性选择合适的插值循环帧数，可以显著优于 MPEG-4 并与常用的 H.264 竞争。

通过时空能量压缩学习图像和视频压缩

Learning Image and Video Compression through Spatial-Temporal Energy  Compaction

The paper addresses the problem of energy compaction of dense 4D light fields
by designing geometry-aware local graph-based transforms. Local graphs are
constructed on super-rays that can be seen as a grouping of spatially and
geometry-dependent angularly correlated pixels. Both non separable and
separable transforms are considered. Despite the local support of limited size
defined by the super-rays, the Laplacian matrix of the non separable graph
remains of high dimension and its diagonalization to compute the transform
eigen vectors remains computationally expensive. To solve this problem, we then
perform the local spatio-angular transform in a separable manner. We show that
when the shape of corresponding super-pixels in the different views is not
isometric, the basis functions of the spatial transforms are not coherent,
resulting in decreased correlation between spatial transform coefficients. We
hence propose a novel transform optimization method that aims at preserving
angular correlation even when the shapes of the super-pixels are not isometric.
Experimental results show the benefit of the approach in terms of energy
compaction. A coding scheme is also described to assess the rate-distortion
perfomances of the proposed transforms and is compared to state of the art
encoders namely HEVC and JPEG Pleno VM 1.1.

本文旨在设计一种局部图形变换，以在不同观测视角下压缩高维光场的能量，通过构造基于超光线的局部图形和局部支持的可分和不可分变换，实现角度相关性的保留和编码效率的提高。