Quality assessment of images and videos emphasizes both local details and
global semantics, whereas general data sampling methods (e.g., resizing,
cropping or grid-based fragment) fail to catch them simultaneously. To address
the deficiency, current approaches have to adopt multi-branch models and take
as input the multi-resolution data, which burdens the model complexity. In this
work, instead of stacking up models, a more elegant data sampling method (named
as SAMA, scaling and masking) is explored, which compacts both the local and
global content in a regular input size. The basic idea is to scale the data
into a pyramid first, and reduce the pyramid into a regular data dimension with
a masking strategy. Benefiting from the spatial and temporal redundancy in
images and videos, the processed data maintains the multi-scale characteristics
with a regular input size, thus can be processed by a single-branch model. We
verify the sampling method in image and video quality assessment. Experiments
show that our sampling method can improve the performance of current
single-branch models significantly, and achieves competitive performance to the
multi-branch models without extra model complexity. The source code will be
available at https://github.com/Sissuire/SAMA.
The Multi-disciplinary Nature of Multimedia Information Systems
Quality assessment of images and videos is a crucial task in the field of multimedia information systems. It requires a deep understanding of both the local details and global semantics of the content. However, traditional data sampling methods like resizing, cropping, or grid-based fragments often fail to capture these aspects simultaneously.
In recent years, there has been a growing trend in adopting multi-branch models to address this deficiency. These models take as input multi-resolution data to capture both local and global content. While effective, this approach leads to increased model complexity and resource requirements.
This article introduces a novel data sampling method called SAMA (Scaling and Masking) that offers a more elegant solution to the problem. SAMA aims to compact both local and global content in a regular input size, without the need for stacking up models.
The underlying idea of SAMA is to first scale the data into a pyramid structure. By reducing this pyramid into a regular data dimension using masking strategies, the processed data maintains its multi-scale characteristics. This compacted data can then be efficiently processed by a single-branch model.
One of the key advantages of SAMA is its ability to leverage the spatial and temporal redundancy present in images and videos. This redundancy allows SAMA to maintain the multi-scale characteristics while reducing the input size, resulting in improved performance without adding extra model complexity.
Relation to Animation, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article have significant relevance to the wider field of Animation, Artificial Reality, Augmented Reality, and Virtual Realities. These fields heavily rely on multimedia content, including images and videos.
In Animation, the quality assessment of visual elements is crucial for creating realistic and immersive environments. By applying the SAMA method to assess the quality of animation frames, animators can ensure that the local details and global semantics are accurately captured, leading to a more authentic animated experience.
Artificial Reality, Augmented Reality, and Virtual Realities often involve the integration of virtual and real-world content. Quality assessment becomes essential when merging these elements seamlessly. SAMA can be instrumental in analyzing and comparing the quality of virtual and real-world images and videos, ensuring that the user experiences a smooth transition without perceptible differences.
Moreover, the multi-disciplinary nature of multimedia information systems allows the adoption of SAMA in a wide range of applications. From video surveillance systems to image recognition algorithms, SAMA’s ability to improve the performance of single-branch models can be harnessed across various domains.
In conclusion, the introduction of SAMA as a data sampling method offers a promising approach to quality assessment in multimedia information systems. Its effectiveness in capturing both local details and global semantics without increasing model complexity makes it a valuable addition to the field. As technology continues to advance, the applications of SAMA in Animation, Artificial Reality, Augmented Reality, and Virtual Realities will undoubtedly expand, leading to enhanced user experiences and improved multimedia content.
Source: https://www.example.com