scholarly journals Rate distortion trade-off in human memory

2019 ◽  
Author(s):  
David G. Nagy ◽  
Balazs Torok ◽  
Gergo Orban
2017 ◽  
Vol 14 (130) ◽  
pp. 20170166 ◽  
Author(s):  
Sarah E. Marzen ◽  
Simon DeDeo

In complex environments, there are costs to both ignorance and perception. An organism needs to track fitness-relevant information about its world, but the more information it tracks, the more resources it must devote to perception. As a first step towards a general understanding of this trade-off, we use a tool from information theory, rate–distortion theory, to study large, unstructured environments with fixed, randomly drawn penalties for stimuli confusion (‘distortions’). We identify two distinct regimes for organisms in these environments: a high-fidelity regime where perceptual costs grow linearly with environmental complexity, and a low-fidelity regime where perceptual costs are, remarkably, independent of the number of environmental states. This suggests that in environments of rapidly increasing complexity, well-adapted organisms will find themselves able to make, just barely, the most subtle distinctions in their environment.


2007 ◽  
Vol E90-D (9) ◽  
pp. 1430-1438 ◽  
Author(s):  
J. XU ◽  
T. YAMASAKI ◽  
K. AIZAWA
Keyword(s):  
3D Video ◽  

Author(s):  
Ismael Ávila ◽  
Ewerton Menezes ◽  
Alexandre Melo Braga

In this chapter, the authors discuss the application of iconic passwords in authentication solutions aimed at the use of smartphones as payment devices. They seek a trade-off between security and usability by means of memorization strategies based on human memory skills. The authors present a first approach to the authentication solution, which was tested with users and compared with a previous scheme that lacked the strategies. The advantages and limitations of the proposed solution, along with future research directions, are then discussed.


Author(s):  
Petros T. Boufounos ◽  
Hassan Mansour ◽  
Shantanu Rane ◽  
Anthony Vetro

Visual retrieval and classification are of growing importance for a number of applications, including surveillance, automotive, as well as web and mobile search. To facilitate these processes, features are often computed from images to extract discriminative aspects of the scene, such as structure, texture or color information. Ideally, these features would be robust to changes in perspective, illumination, and other transformations. This paper examines two approaches that employ dimensionality reduction for fast and accurate matching of visual features while also being bandwidth-efficient, scalable, and parallelizable. We focus on two classes of techniques to illustrate the benefits of dimensionality reduction in the context of various industrial applications. The first method is referred to as quantized embeddings, which generates a distance-preserving feature vector with low rate. The second method is a low-rank matrix factorization applied to a sequence of visual features, which exploits the temporal redundancy among feature vectors associated with each frame in a video. Both methods discussed in this paper are also universal in that they do not require prior assumptions about the statistical properties of the signals in the database or the query. Furthermore, they enable the system designer to navigate a rate versus performance trade-off similar to the rate-distortion trade-off in conventional compression.


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