A Hitchhiker’s Guide to Structural Similarity

Venkataramanan, Abhinau K.; Wu, Chengyang; Bovik, Alan C.; Katsavounidis, Ioannis; Shahid, Zafar

doi:10.1109/access.2021.3056504

Cited by 42 publications

(42 citation statements)

References 51 publications

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“…DLM 2. Structural Similarity (SSIM) [19] or Enhanced SSIM (ESSIM) [20] computed in the wavelet domain (WD-SSIM and WD-ESSIM), as described in Section 3.2.…”

Section: Algorithmmentioning

confidence: 99%

“…The effectiveness of spatial CSF filtering is demonstrated in Section 5.1. The ESSIM model [20] improves upon SSIM by using small, strided rectangular windows to compute local quality scores, Coefficient of Variation (CoV) pooling for spatial aggregation, and the Self-Adaptive Scale Transform (SAST) to rescale frames before quality assessment. However, since SAST must be applied commonly to all atom features, its use is investigated as a global design choice in Section 4.…”

Section: Unified Transformmentioning

confidence: 99%

“…and C ∈ {H, V, D} denotes a subband. These local statistics may be used to compute both WD-SSIM and WD-ESSIM, as in [20].…”

Section: Unified Transformmentioning

confidence: 99%

“…In addition to the use of a unified transform and computing SSIM directly from wavelet coefficients, integral images [23] have been used to effectively minimize the number of convolution operations. Specifically, we have optimized the computation of local statistics for VIF using integral images, instead of convolution, as in [20]. Furthermore, the non-separable 3 × 3 kernel used in DLM has been decomposed as shown in Figure 2.…”

Section: Integral Image-based Optimizationmentioning

confidence: 99%

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FUNQUE: Fusion of Unified Quality Evaluators

Venkataramanan¹,

Stejerean²,

Bovik³

2022

Preprint

Self Cite

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Fusion-based quality assessment has emerged as a powerful method for developing high-performance quality models from quality models that individually achieve lower performances. A prominent example of such an algorithm is VMAF, which has been widely adopted as an industry standard for video quality prediction along with SSIM. In addition to advancing the state-of-the-art, it is imperative to alleviate the computational burden presented by the use of a heterogeneous set of quality models. In this paper, we unify "atom" quality models by computing them on a common transform domain that accounts for the Human Visual System, and we propose FUNQUE, a quality model that fuses unified quality evaluators. We demonstrate that in comparison to the stateof-the-art, FUNQUE offers significant improvements in both correlation against subjective scores and efficiency, due to computation sharing.

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“…DLM 2. Structural Similarity (SSIM) [19] or Enhanced SSIM (ESSIM) [20] computed in the wavelet domain (WD-SSIM and WD-ESSIM), as described in Section 3.2.…”

Section: Algorithmmentioning

confidence: 99%

Section: Unified Transformmentioning

confidence: 99%

“…and C ∈ {H, V, D} denotes a subband. These local statistics may be used to compute both WD-SSIM and WD-ESSIM, as in [20].…”

Section: Unified Transformmentioning

confidence: 99%

Section: Integral Image-based Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

FUNQUE: Fusion of Unified Quality Evaluators

Venkataramanan¹,

Stejerean²,

Bovik³

2022

Preprint

Self Cite

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show abstract

“…Regardless of the source of the discrepancy between images, IQA measures provide an objective way of quantifying that difference. Perhaps the most well-known and commonly used IQA measure is the Structural Similarity Index (SSIM) [1], [2], though a number of such image-comparison techniques have been developed (e.g., [3]- [7]).…”

Section: Introductionmentioning

confidence: 99%

DSSIM: a structural similarity index for floating-point data

Baker¹,

Pinard²,

Hammerling³

2022

Preprint

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Data visualization is a critical component in terms of interacting with floating-point output data from large model simulation codes. Indeed, postprocessing analysis workflows on simulation data often generate a large number of images from the raw data, many of which are then compared to each other or to specified reference images. In this image-comparison scenario, image quality assessment (IQA) measures are quite useful, and the Structural Similarity Index (SSIM) continues to be a popular choice. However, generating large numbers of images can be costly, and plot-specific (but data independent) choices can affect the SSIM value. A natural question is whether we can apply the SSIM directly to the floating-point simulation data and obtain an indication of whether differences in the data are likely to impact a visual assessment, effectively bypassing the creation of a specific set of images from the data. To this end, we propose an alternative to the popular SSIM that can be applied directly to the floatingpoint data, which we refer to as the Data SSIM (DSSIM). While we demonstrate the usefulness of the DSSIM in the context of evaluating differences due to lossy compression on large volumes of simulation data from a popular climate model, the DSSIM may prove useful for many other applications involving simulation or image data.

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Channel equalization through pre‐denoising using a hybrid multiscale decomposition in an impulsive noise environment

Wilson,

Panigrahi,

Mishra

et al. 2024

Int J Communication

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SummaryIn wireless communication, impulsive noise often degrades channel quality, which poses challenges for equalizers. Although robust equalization methods offer some effectiveness, the occurrence of impulsive noise after training significantly impacts the symbol error rate (SER). To mitigate this issue, we propose a method that involves denoising the received signal using robust wavelet decomposition before equalization. This approach combines the discrete wavelet transform with median and morphological filters to reduce impulsive noise. A pre‐impulsive noise detection mechanism triggers denoising only when impulsive noise is detected. We evaluate the SER performance of the proposed technique using simulations of a 16‐QAM orthogonal frequency division multiplexing (OFDM) system with kernel interpolation‐based frequency domain equalization (FDE) in a Rayleigh fading channel with impulsive noise. Results show that our approach achieves SER levels comparable to conventional methods in Gaussian noise scenarios, demonstrating its effectiveness in challenging wireless communication environments. The simulation results demonstrate that the proposed technique in non‐Gaussian noise gives the SER on par with the FDE in a Gaussian noise environment.

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A Hitchhiker’s Guide to Structural Similarity

Cited by 42 publications

References 51 publications

FUNQUE: Fusion of Unified Quality Evaluators

FUNQUE: Fusion of Unified Quality Evaluators

DSSIM: a structural similarity index for floating-point data

Channel equalization through pre‐denoising using a hybrid multiscale decomposition in an impulsive noise environment

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