Image quality assessment is a fundamental problem in the field of image
processing, and due to the lack of reference images in most practical
scenarios, no-reference image quality assessment (NR-IQA), has gained
increasing attention recently. With the development of deep learning
technology, many deep neural network-based NR-IQA methods have been developed,
which try to learn the image quality based on the understanding of database
information. Currently, Transformer has achieved remarkable progress in various
vision tasks. Since the characteristics of the attention mechanism in
Transformer fit the global perceptual impact of artifacts perceived by a human,
Transformer is thus well suited for image quality assessment tasks. In this
paper, we propose a Transformer based NR-IQA model using a predicted objective
error map and perceptual quality token. Specifically, we firstly generate the
predicted error map by pre-training one model consisting of a Transformer
encoder and decoder, in which the objective difference between the distorted
and the reference images is used as supervision. Then, we freeze the parameters
of the pre-trained model and design another branch using the vision Transformer
to extract the perceptual quality token for feature fusion with the predicted
error map. Finally, the fused features are regressed to the final image quality
score. Extensive experiments have shown that our proposed method outperforms
the current state-of-the-art in both authentic and synthetic image databases.
Moreover, the attentional map extracted by the perceptual quality token also
does conform to the characteristics of the human visual system.

本文提出了一种基于 Transformer 的无参考图像质量评估模型，使用预测的目标误差地图和感知质量令牌进行特征融合，比现有技术表现更好，提高了对真实和合成图像数据库的性能表现。同时，通过感知质量令牌提取的注意力图也符合人类视觉系统的特征。

通过 Transformer 预测错误图和感知质量令牌进行盲图像质量评估

Blind Image Quality Assessment via Transformer Predicted Error Map and  Perceptual Quality Token

Machine learning tasks over image databases often generate masks that
annotate image content (e.g., saliency maps, segmentation maps) and enable a
variety of applications (e.g., determine if a model is learning spurious
correlations or if an image was maliciously modified to mislead a model). While
queries that retrieve examples based on mask properties are valuable to
practitioners, existing systems do not support such queries efficiently. In
this paper, we formalize the problem and propose a system, MaskSearch, that
focuses on accelerating queries over databases of image masks. MaskSearch
leverages a novel indexing technique and an efficient filter-verification query
execution framework. Experiments on real-world datasets with our prototype show
that MaskSearch, using indexes approximately 5% the size of the data,
accelerates individual queries by up to two orders of magnitude and
consistently outperforms existing methods on various multi-query workloads that
simulate dataset exploration and analysis processes.

本文提出了一个名为 MaskSearch 的系统，用于加速图像掩码数据库上的查询，并且通过一种新颖的索引技术和高效的查询执行框架，MaskSearch 能够在数据所占比例仅为 5% 的情况下加速两个数量级的单个查询，并在各种模拟数据集探索和分析过程的多查询负载下一致优于现有的方法。

大规模查询图像遮罩

MaskSearch: Querying Image Masks at Scale

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。