We study domain-specific video streaming. Specifically, we target a streaming
setting where the videos to be streamed from a server to a client are all in
the same domain and they have to be compressed to a small size for low-latency
transmission. Several popular video streaming services, such as the video game
streaming services of GeForce Now and Twitch, fall in this category. While
conventional video compression standards such as H.264 are commonly used for
this task, we hypothesize that one can leverage the property that the videos
are all in the same domain to achieve better video quality. Based on this
hypothesis, we propose a novel video compression pipeline. Specifically, we
first apply H.264 to compress domain-specific videos. We then train a novel
binary autoencoder to encode the leftover domain-specific residual information
frame-by-frame into binary representations. These binary representations are
then compressed and sent to the client together with the H.264 stream. In our
experiments, we show that our pipeline yields consistent gains over standard
H.264 compression across several benchmark datasets while using the same
channel bandwidth.

本文研究了域特定视频流媒体，提出了一种新颖的视频压缩方法来提高视频质量并实现低时延传输，该方法利用了域内视频的属性，通过将 H.264 与二进制自动编码器相结合，实现了恰当的编码和传输，且在标准数据集上都表现出了良好的效果。

学习二进制残差表示在特定领域视频流传输中的应用

Learning Binary Residual Representations for Domain-specific Video  Streaming

In most state-of-the-art hashing-based visual search systems, local image
descriptors of an image are first aggregated as a single feature vector. This
feature vector is then subjected to a hashing function that produces a binary
hash code. In previous work, the aggregating and the hashing processes are
designed independently. In this paper, we propose a novel framework where
feature aggregating and hashing are designed simultaneously and optimized
jointly. Specifically, our joint optimization produces aggregated
representations that can be better reconstructed by some binary codes. This
leads to more discriminative binary hash codes and improved retrieval accuracy.
In addition, we also propose a fast version of the recently-proposed Binary
Autoencoder to be used in our proposed framework. We perform extensive
retrieval experiments on several benchmark datasets with both SIFT and
convolutional features. Our results suggest that the proposed framework
achieves significant improvements over the state of the art.

本文提出了一种新颖的框架，其中功能聚合和哈希同时设计并联合优化，目的是生成更具区分度的二进制哈希码从而提高图像的检索精度。同时，我们还提出了一个快速版本的二进制自编码器用于我们的提出的框架中。在多个基准数据集上进行的广泛检索实验表明，所提出的框架显著改善了最先进技术水平。

用于大规模图像搜索的同时特征聚合和哈希

Simultaneous Feature Aggregating and Hashing for Large-scale Image  Search

An attractive approach for fast search in image databases is binary hashing,
where each high-dimensional, real-valued image is mapped onto a
low-dimensional, binary vector and the search is done in this binary space.
Finding the optimal hash function is difficult because it involves binary
constraints, and most approaches approximate the optimization by relaxing the
constraints and then binarizing the result. Here, we focus on the binary
autoencoder model, which seeks to reconstruct an image from the binary code
produced by the hash function. We show that the optimization can be simplified
with the method of auxiliary coordinates. This reformulates the optimization as
alternating two easier steps: one that learns the encoder and decoder
separately, and one that optimizes the code for each image. Image retrieval
experiments, using precision/recall and a measure of code utilization, show the
resulting hash function outperforms or is competitive with state-of-the-art
methods for binary hashing.

通过使用二元哈希算法，在二进制空间内搜索图像数据库，本文提出使用二元自编码器模型简化哈希函数的优化问题，并通过精度 / 召回率的实验结果证明二元自编码器模型相较其他二元哈希算法更加优秀。