Today Finish:

  1. Review Paper No-Reference Quality Assessment for Screen Content Images Based on Hybrid Region Features Fusion

    (1) SEPes:

    SEPes: text, computer graphics and high-frequency part of pictorial region in SCI

    Non-SEPes: smooth and background areas with noise (the distribution of non-SEP is uniform, i.e. the intensity of image changes slowly, and the distortions are smoothed → relatively small effect)

    A. Sharp edge patches (SEPes) <- variance of local standard deviation

    B. Feature Extraction:

    entropy and contrast features from a gray-level co-occurrence matrix → local phase coherence

    local phase coherence → capture the loss in sharpness

    C. Average pooling → fuse features obtained from all of the SEPes to represent the local features

    D. BRISQUE method → combine local features with global features → hybrid region (HR)-based features

    (2) Some Terminologies:

    GLCM: Gray-level Co-occurrence Matrix

    LPC: local phase coherence

    LSD Distribution: local standard deviation distribution (The LSD indicates the image structural complexity in a local area, which also highlights the details of the edge and smoothes out the effects of distortion.)

    (After being contaminated by severe Gaussian noise, the characteristic of the LSD distribution of the SEP and non-SEP can still be roughly maintained)

    VOLSD: variance of local standard deviation

    implement the classification of SCIs regions

    (3) Feature Extraction:

    A. Motivation:

    a. The analysis of SCIs microstructure is very essential

    b. The texture is concerned as the spatial distribution and spatial dependence among the graylevel in a local area.

    (4) Some Characteristics & Properties of SCIs:

    A. Different regions of SCIs present dissimilar peculiarity

    B. HVS is highly sensitive to image contents containing sharp edges,

    C. Sharp edges are common part of SCIs

    (5) MSCN Histogram:

    BRISQUE first estimates parameters of the presumed generalized Gaussian distribution (GGD) or asymmetric generalized Gaussian distribution (AGGD) by fitting histogram of mean subtracted contrast normalized (MSCN) coefficients or pairwise products of MSCN coefficients from neighboring pixels.

    (6) Some Conclusions:

    A. Influence of Gaussian Window Size

    7 X 7 Gaussian window size

    B. Influence of Patches Number

    More patches, better performance

    set 100 patches

    Different image contents are contained in the same patch create the impressive influences when features extraction are conducted.

  1. Read Paper Blindly Assess Image Quality in the Wild Guided by A Self-Adaptive Hyper Network

(1) Authentically Distorted Images Properties:

A. distortion diversity

B. content variation

C. local distortions

(2) Authentic Distortions:

A. Global uniform distortions (e.g. out of focus, low illumination)

B. Contain other kinds of non-uniform distortions (e.g. object moving, over lighting, ghosting) in local areas

(3) Authentic IQA database:

A. LIVE Challenge (LIVEC)

LIVEC contains 1162 images taken from different photographers with varies camera devices in the real world, hence these images contain complex and composite distortions.

B. KonIQ-10k

KonIQ-10k consists of 10073 images which are selected from the large public multimedia database YFCC100m, the sampled images try to cover a wide and uniform quality distribution in the sense of brightness, colorfulness, contrast and sharpness.


BID is a blur image database containing 586 images with realistic blur distortions such as motion blur and out of focus, etc.

(4) Synthetic IQA Field:

A. hand-crafted feature based IQA - NSS models to capture distortion

(a) Quality aware natural scene parameters include discrete wavelet coefficients, the correlation coefficients across subbands, DCT coefficients, locally normalized luminance coefficients with their pairwise products, image gradient, log-Gabor responses and color statistics.

(b) Distribution models used to capture the statistics from synthetically distorted image include generalized Gaussian distribution (GGD), asymmetric generalized Gaussian distribution (AGGD), Weibull distribution, third order polynomial tting and histogram counting.

B. learning feature based IQA

(a) Codebook based learning approach

(b) CNN based methods

Semantic Features from the Pretrained baseline network: VGG-16, ResNet-50, ResNet-34


<1> ignore how image semantics influence quality perception as mixing semantic learning and quality prediction in one network

<2> local distortions are ignored as deep semantic features are extracted from global scale

(5) Experiments

A. Single database evaluation

B. Generalization ability test (cross database test)

Tomorrow Work:

  1. Review SC Images Codes
  2. Continue to read the above paper