Signal Processing - July 2017 - 84
A typical pipeline
robust enough for the intertwined aesthetic rules. The majority of feature types can be classified into handcrafted features and deep features. Conventional approaches [20], [21],
[37]-[49] typically adopt handcrafted features to computationally model the photographic rules (e.g., lighting and
contrast), global image layout (the rule of thirds), and typical objects (e.g., human profiles, animals, and plants) in
images. In more recent work, generic deep features [50], [51]
and learned deep features [23]-[25], [52]-[59] exhibit stronger representation power for this task.
Most existing image-quality assessment methods take a supervised learning approach. A typical pipeline assumes a set of
training data " x i, y i ,i ! [1, N], from which a function f: g (X) " Y
is learned, where g (x i) denotes the feature representation of
image x i. The label y i is either {0, 1} for binary classification
(when f is a classifier) or a continuous score range for regression (when f is a regressor). Following this formulation, a
pipeline can be broken into two main components, as shown
in Figure 1(b), i.e., a feature extraction component and a decision component.
Decision phase
Feature extraction
The second component of an image aesthetics assessment system provides the ability to perform classification or regression
for the given aesthetic task. The naïve Bayes classifier, SVM,
boosting, and deep classifier are typically used for binary classification of high-quality and low-quality images, whereas
regressors like support vector regressors (SVRs) are used in
ranking or scoring images based on their aesthetic quality.
The first component of an image aesthetics assessment system aims at extracting robust feature representations
describing the aesthetic aspect of an image. Such features
are assumed to model the photographic/artistic aspect of
images to distinguish images of different qualities. Nu merous efforts have been made to design features that are
Reference
PSNR/SSIM/VIF
Gaussian Blur, σ = 1
26.19/0.86/0.48
Reference
PSNR/SSIM/VIF
High-Quality Image
7.69/-0.13/0.04
Gaussian Blur, σ = 2
22.71/0.72/0.22
(a)
Low-Quality Image
8.50/0.12/0.03
(b)
FIGURE 2. Quality measurements by peak signal-to-noise ratio (PSNR), SSIM [31], and VIF [32] (a higher measurement is better, typically made against a
referencing ground-truth high-quality image). Although these are good indicators for measuring the quality of images in image restoration applications,
such as the images in (a), they do not reflect human-perceived aesthetic values, as shown by the measurements for the building images in (b).
84
IEEE SIGNAL PROCESSING MAGAZINE
|
July 2017
|
Table of Contents for the Digital Edition of Signal Processing - July 2017
Signal Processing - July 2017 - Cover1
Signal Processing - July 2017 - Cover2
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Signal Processing - July 2017 - Cover3
Signal Processing - July 2017 - Cover4
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