Disc-Cover Complexity Trends in Music Illustrations from Sinatra to Swift

TL;DR

This study investigates the evolution of album cover visuals by applying entropy-based and compression-based complexity measures ($H$, $C$, $MDLc$, $ZIPc$) to a dataset of $46{,}399$ covers spanning 75 years and 11 genres. A data pipeline aggregates MuMu, MSD-I, and Billboard covers, standardizes genres to 11 supergenres using LLMs, and uses automated imputation for missing labels. Findings show a general shift toward visual minimalism over time, with notable outliers in Metal and Hip Hop, and increasing dispersion indicating polarization of styles in the digital era. By combining perceptual metrics with semantic content via YOLOv8, the study links aesthetic evolution to platform-driven visibility and cultural dynamics, offering a scalable approach for cultural analytics.

Abstract

The study of art evolution has provided valuable insights into societal change, often revealing long-term patterns of simplification and transformation. Album covers represent a distinctive yet understudied form of visual art that has both shaped and been shaped by cultural, technological, and commercial dynamics over the past century. As highly visible artifacts at the intersection of art and commerce, they offer a unique lens through which to study cultural evolution. In this work, we examine the visual complexity of album covers spanning 75 years and 11 popular musical genres. Using a diverse set of computational measures that capture multiple dimensions of visual complexity, our analysis reveals a broad shift toward minimalism across most genres, with notable exceptions that highlight the heterogeneity of aesthetic trends. At the same time, we observe growing variance over time, with many covers continuing to display high levels of abstraction and intricacy. Together, these findings position album covers as a rich, quantifiable archive of cultural history and underscore the value of computational approaches in the systematic study of the arts, bridging quantitative analysis with aesthetic and cultural inquiry.

Figures (6)

• Figure 1: Album cover examples with different complexity and entropy scores. (top left) Pink Floyd, The Dark Side of the Moon, 1973. (top right) Bob Dylan, Blonde on Blonde, 1966. (c) Whiskey Myers, Whiskey Myers, 2019. (d) By the Way, Red Hot Chili Peppers, 2002.
• Figure 2: Characterization of album covers in the Entropy-Complexity plane. Panel (a) shows the average positioning for each music genre, highlighting a central cluster and key outliers. Panel (b) illustrates the aggregate temporal evolution across all genres.
• Figure 3: Temporal evolution of average visual complexity across genres, measured by (a) MDLc and (b) ZIPc. Periods were defined of variable duration to ensure a minimum of 3000 albums per period (except the final period, which contains 2,269). A genre is included in a period only if at least 50 albums of that genre are present.
• Figure 4: Evolution of the distribution of complexity scores over time for (a) the MDLc metric and (b) the ZIPc metric. While the median complexity (red dashed line) trends downward in recent years, the interquartile range (height of the boxes) increases, indicating growing stylistic diversity.
• Figure 5: Analysis of semantic content via object detection across music genres. Panel (a) shows the proportional distribution of detected object classes per genre, including albums with no detected objects. The 'person' class predominates across all genres, while Electronic and Metal show higher proportions of albums with no detected objects. Panel (b) illustrates the temporal evolution of average object counts per album. Metal consistently exhibits the lowest object count despite high visual complexity, while Electronic maintains low counts consistent with its minimalist aesthetic.