Measuring meaningful information in images: algorithmic specified complexity

Ewert, Winston; Dembski, William A.; Marks, Robert J.

doi:10.1049/iet-cvi.2014.0141

Cited by 3 publications

(2 citation statements)

References 70 publications

(131 reference statements)

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“…One useful information-theoretical metric is redundancy reduction. Algorithms that detect redundancy have been used for some time to compress the size of computer files (e.g., image compression; Ewert, Dembski, & Marks, 2015;Feldman & Crutchfield, 1998). In one study, image compressibility was considered to define complexity (less compressible is more complex).…”

Section: Introductionmentioning

confidence: 99%

The sound of beauty: How complexity determines aesthetic preference

et al. 2019

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Stimulus complexity is an important determinant of aesthetic preference. An influential idea is that increases in stimulus complexity lead to increased preference up to an optimal point after which preference decreases (inverted-U pattern). However, whereas some studies indeed observed this pattern, most studies instead showed an increased preference for more complexity. One complicating issue is that it remains unclear how to define complexity. To address this, we approached complexity and its relation to aesthetic preference from a predictive coding perspective. Here, low-and high-complexity stimuli would correspond to low and high levels of prediction errors, respectively. We expected participants to prefer stimuli which are neither too easy to predict (low prediction error), nor too difficult (high prediction error). To test this, we presented two sequences of tones on each trial that varied in predictability from highly regular (low prediction error) to completely random (high prediction error), and participants had to indicate which of the two sequences they preferred in a two-interval forced-choice task. The complexity of each tone sequence (amount of prediction error) was estimated using entropy. Results showed that participants tended to choose stimuli with intermediate complexity over those of high or low complexity. This confirms the century-old idea that stimulus complexity has an inverted-U relationship to aesthetic preference.

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Section: Introductionmentioning

confidence: 99%

The sound of beauty: How complexity determines aesthetic preference

et al. 2019

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“…In the literature, there has been recent interest in the complexity of media content in various forms. Image complexity has been measured by a variety of metrics, including fractal dimension [58], algorithmic specified complexity [59], compressed file size [60], the degree of flatness in the profile of gray-scale mean variance [61], and the entropy of luminosity [48]. In addition, the complexities of text discourse [62], movie narration [63], and the rhythm of films [64] have been investigated.…”

Section: Statistical Complexity As a Characteristic Of Emotion Dismentioning

confidence: 99%

Exploration of the Characteristics of Emotion Distribution in Korean TV Series: Common Pattern and Statistical Complexity

Kim

Lee

2020

IEEE Access

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The series is one of the most popular formats of television programs, but the emotional aspects of its content have not attracted much attention from researchers. Furthermore, the cinemetric approach, in which move data are quantitively examined, has not been used to analyze affective content in television series. Therefore, this study focused on emotion distribution in Korean television series. In all, 337 episodes from 20 series were divided into shots, then keyframes were selected from the shots. Facial emotions on the keyframes were measured using an online artificial intelligence service. Emotion distributions were obtained from each episode and then were observed for common patterns. In addition, the statistical complexity of the distributions were calculated and compared by the temporal phase of the episode, channel type, genre, ratings, and season. The results show that the emotion distributions of all episodes in all series had the same form: an asymmetric U-shaped distribution biased toward zero. The statistical complexity of emotion distribution was greater in early episodes than in later episodes of a series, and it was higher in episodes with higher ratings. In addition, the complexity of emotion distribution was higher in hard genres and during the summer season. There was no significant difference between terrestrial television channels and cable/general programming channels.

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Order, organization, and randomness: on the mathematical formulation of life

Cosgrove

2024

Synthese

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Measuring meaningful information in images: algorithmic specified complexity

Cited by 3 publications

References 70 publications

The sound of beauty: How complexity determines aesthetic preference

The sound of beauty: How complexity determines aesthetic preference

Exploration of the Characteristics of Emotion Distribution in Korean TV Series: Common Pattern and Statistical Complexity

Order, organization, and randomness: on the mathematical formulation of life

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