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REVIEW 1 major objections 5 minor 43 references

Impacts of Retina-related Zones on Quality Perception of Omnidirectional Image

T0 review · 1 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Fovea and parafovea dominate perceived 360-degree image quality.

desk verdict This paper has a genuinely useful new database and a plausible qualitative finding, but the fitted zone weights in Table 4 are under-identified and the numerical values should not be used prescriptively. read the letter →

arxiv 1908.06239 v1 pith:TOMKJUUW submitted 2019-08-17 eess.IV cs.MM

Huyen T. T. Tran , Duc V. Nguyen , Nam Pham Ngoc , Trang H. Hoang , Truong Thu Huong , Truong Cong Thang This is my paper · ORCID
classification eess.IVcs.MM
keywords omnidirectionalimagesvirtualrealityqualityperceptionretinalzonesfoveafoveatedrenderingsubjectiveassessmentobjectivemetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that, for 360-degree images displayed in VR, perceptual quality is determined almost entirely by the retinal zones surrounding the foveation point—the fovea and parafovea within about 4 degrees of gaze—and that the perifovea and periphery contribute little. The authors support this with a subjective experiment on 256 non-uniform-quality stimuli from eight scenes, where each viewport is divided into five zones matching retina regions, and with a fitted zone-weighted error model. Fitted weights put the combined central 4-degree zones at 0.737 or more of total weight in every image and every zone beyond 4 degrees at 0.095 or less; the model reproduces per-image mean opinion scores with Pearson correlation at least 0.97. They also report that nineteen existing objective metrics, including foveal metrics, all fall below PCC 0.70 on the full database, so current metrics do not reflect how viewers experience spatially varying quality in VR.

What carries the argument

The load-bearing object is the zone-weighted formulation (ZWF), a simple eccentricity-weighted MSE: $\mathrm{ZWF}=10\log_{10}\left(\frac{\mathrm{MAX}^2}{\sum_{k=1}^{5} w_k \mathrm{MSE}_k}\right)$, where $\mathrm{MSE}_k$ is the mean squared error of pixels whose eccentricity falls in zone $Z_k$ and $w_k$ is the fitted importance of that zone. The eccentricity of each pixel is computed from a VR lens model (Eqs. 1–12), so pixels are assigned to the five retinal-zone intervals rather than to arbitrary rings. Fitting the five weights and the five parameters of the logistic mapping together by least squares is what turns subjective ratings into the per-zone importance table; the high per-image correlation (PCC $\ge 0.97$) is the evidence that the weights carry the perceptual signal.

What would settle it

Run the same rating experiment with eye tracking and record where participants actually fixate during each five-second judgment. If fixations frequently leave the central 2.5-degree zone, or if recomputing zone weights from measured gaze shifts substantial weight beyond 4 degrees, then the reported fovea/parafovea dominance is an artifact of the fixation assumption rather than a property of retinal-zone perception.

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Extended reading notes

Core claim

The central discovery is a quantitative map of where quality matters in an omnidirectional viewport. The authors asked 62 participants to rate stimuli whose five concentric zones—$Z_1$ $[0,2.5^\circ)$ fovea, $Z_2$ $[2.5^\circ,4^\circ)$ parafovea, $Z_3$ $[4^\circ,9^\circ)$ perifovea, $Z_4$ $[9^\circ,30^\circ)$ near periphery, and $Z_5$ $[30^\circ,\infty)$ far periphery—were either high or low quality in eight spatial patterns and four blur levels. Fitting weights $w_k$ in the zone-weighted formulation $\mathrm{ZWF}=10\log_{10}\left(\mathrm{MAX}^2/\sum_{k=1}^{5} w_k\,\mathrm{MSE}_k\right)$, with a five-parameter logistic mapping to MOS, gave per-image weights in which $w_1$ is usually the largest (0.404 to 0.941), the fovea-plus-parafovea share $w_1+w_2$ is at least 0.737 in every image, and all weights for zones beyond 4 degrees are at most 0.095. Pearson correlation between the fitted model and MOS is at least 0.97 per image and RMSE at most 0.27. The weights also vary with content: images with a small attractive central face put almost all weight in $Z_1$, while images with a large or poorly contrasting central object distribute weight between $Z_1$ and $Z_2$. On the same database, all nineteen tested objective quality metrics achieved PCC below 0.70 after logistic mapping, so none captured this non-uniform-quality perception.

Load-bearing premise

The load-bearing premise is that participants truly fixated the viewport center during each five-second rating and that the chosen retinal zone boundaries are accurate, so the fitted weights reflect retinal-zone importance rather than where people happened to look.

Editorial extensions

If this is right

  • A foveated or viewport-adaptive 360-degree encoder can spend most of its bit budget on the central 4 degrees of the user's viewport and expect little perceived quality loss from blurring the periphery.
  • Any objective quality metric for omnidirectional content with spatially varying quality should weight pixel errors by eccentricity, and likely by content-specific attention; unweighted viewport metrics will mispredict mean opinion scores.
  • Perceived quality of non-uniform stimuli can be predicted with a simple weighted-MSE model rather than complex structural or foveal metrics, once per-image weights are known.
  • Content characteristics—particularly the size and attractiveness of the central object and the presence of nearby objects—must enter the model, since fitted central-zone weights range from 0.404 to 0.941 across scenes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension of the paper's weighting machinery to gaze-tracked free viewing: the effective 'central region' should expand or shift toward salient objects, so fitted weights would track attended area rather than strict retinal anatomy.
  • The paper's own caveat implies the numeric weight table is evidence for monotone central dominance, not exact anatomical constants, since the zone boundaries at 2.5, 4, 9, and 30 degrees are not standardized.
  • Extrapolating to video, a streaming system could adapt per-tile quality to the current viewport center using the same per-image fitting procedure as a per-clip calibration step.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 5 minor

Summary. The paper studies how spatially non-uniform quality in omnidirectional images affects perceived quality, with the viewport divided into five retinal-zone-inspired concentric regions (fovea, parafovea, perifovea, near periphery, far periphery). A subjective experiment with 62 participants produced MOS scores for 256 stimuli (8 source images, 8 quality-variation patterns, 4 blur levels). The authors propose a zone-weighted formulation (ZWF) combined with a five-parameter logistic mapping, fit per image to the MOS data, yielding fitted zone weights w1...w5. They report that the foveal and parafoveal zones dominate perceived quality, that content characteristics modulate these weights, and that nineteen objective quality metrics, including foveal metrics, correlate poorly with the subjective scores. The paper also details the VR viewing geometry and retina region boundaries used to define the zones.

Significance. If the qualitative conclusion is correct, the paper provides useful evidence for foveated rendering and viewport-adaptive streaming of omnidirectional content, and the new subjective database is a resource for the community. The evaluation of nineteen objective metrics on non-uniform-quality omnidirectional content is also a contribution. However, the quantitative zone weights in Table 4 are the output of a fitting procedure with identifiability problems, and the paper's claim that the fitting is 'reliable' is based on in-sample correlation. The qualitative direction of the result is plausible and consistent with prior work, but the specific numerical claims about individual zone weights are not established by the current analysis.

major comments (1)
  1. [4.1-4.2, Tables 4-5] The interpretation of Table 4 as retinal-zone importance depends on the assumption that each participant maintained stable fixation at the viewport center throughout the rating, as described in Section 3. However, gaze was not tracked. The authors themselves note in Section 4.2 that for images I2 and I6, participants likely looked at a large central area rather than zone Z1 alone, which contradicts the fixed-foveation premise underlying Eqs. (11)-(12) that places the foveation point at the viewport center for all stimuli. If fixation drifted toward nearby attractive objects, the eccentricity assignments and hence the fitted weights in Table 4 are not valid measurements of retinal-zone importance. This is a load-bearing assumption for the paper's central claim and should be addressed, for example by reporting eye-tracking data or by softening the retinal-zone interpretation.
minor comments (5)
  1. [Eq. (11)] Equation (11) contains a typographical artifact ('vu√') before the square root; the formula should simply read d' = sqrt( ... ).
  2. [Section 2.2] The description of the fovea as 'represents 5 degrees of the central visual field or an eccentricity interval between 0 degree and 2.5 degrees' is geometrically correct but could be clarified by stating explicitly that 5 degrees is the full angular diameter and the zone is [0, 2.5) degrees of eccentricity.
  3. [Table 6] The descriptions of MSE and VPSNR are identical ('calculated based on visible pixels of a viewport with equal weights'); the distinction between raw mean squared error and peak-signal-to-noise ratio should be stated explicitly.
  4. [Section 5.1] For the metrics implemented by the authors (FWQI, FWSNR, FPSNR, F-SSIM), the text says they are based on the corresponding publications but does not provide implementation details or validation against the original authors' code; a brief note on verification would improve reproducibility.
  5. [Section 4.2] The claims linking the variation of w1 to the attractiveness and size of central objects are post hoc interpretations without quantitative support; consider presenting them as hypotheses rather than conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the zone weights are presented as fitted estimates from the subjective experiment, not as predictions derived from their own definition.

full rationale

The paper's central zone-importance claim is the output of a least-squares fit of the zone-weighted formulation (Eq. 17) and the logistic mapping (Eq. 18) to the collected MOS values. Reporting the fitted weights as quantified impacts is an empirical measurement, not a circular derivation: the conclusion is not an input to the model, and no equation is defined in terms of the claimed result. The high PCC and low RMSE are computed on the same data used for fitting, so the statement that the fit is 'reliable' is an in-sample validation weakness rather than a circular reduction. The retina zone boundaries and eccentricity geometry are taken from cited anatomical and optical sources, and the logistic mapping is supported by an independent standard reference [33] in addition to the authors' own prior work [22], so self-citation is not load-bearing. Concerns about unverified gaze fixation, non-standard zone boundaries, and under-identification of outer-zone weights are validity or identifiability threats, not circularity. No fitted parameter is relabeled as an independent prediction, and no self-citation is used to forbid alternative interpretations. Therefore, no circular step meeting the required standard is present.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on per-image fitted parameters (five zone weights in Table 4 plus five logistic coefficients from Eq. (18) per fit), on hand-chosen blur levels, and on five domain assumptions: nonstandard retina boundaries, stable gaze, logistic mapping validity, blending-belt effectiveness, and the lens-geometry constants. No new physical entities are introduced. The weights are the most consequential: they are the quantitative claim, and they are fit to the same MOS they summarize.

free parameters (3)
  • Per-image zone weights w1..w5 = Range w1: 0.404 (I6) to 0.941 (I7); w1+w2 >= 0.737; w3-w5 <= 0.095 (Table 4)
    Least-squares fit to per-image MOS via Eq. (18); the zone-importance conclusion is read directly off these fitted values, with no cross-validation or confidence intervals. Near-equal values for I3, I5, and I7 suggest weak identifiability.
  • Logistic mapping coefficients beta1..beta5 = Not reported numerically
    Five-parameter logistic (Eq. (18)) fit per image for ZWF and per metric for evaluation; standard in IQA (Sheikh et al. [33]) but inflates the number of fitted parameters to about nine per image for only 32 stimuli.
  • Gaussian blur levels (sigma) = S#1: 2, 4, 8, 12; S#2: 1, 2, 4, 6
    Hand-chosen to span visible to severe blur; different sets for the two scenarios complicate direct comparison of scenario effects on the fitted weights.
assumptions (5)
  • domain assumption Retina zone boundaries: fovea [0,2.5), parafovea [2.5,4), perifovea [4,9), near periphery [9,30), far periphery [30,+infinity) degrees
    Adopted from refs [26-29]; the paper itself notes 'there has been no standard definition of boundaries between these regions so far [26]'. All fitted weights are conditioned on these cut-offs, so a different boundary convention changes the zone-importance numbers.
  • domain assumption Participants kept gaze fixed at the viewport center during rating
    Sec. 3 instructs participants to look straight ahead at the center, but gaze is not tracked. The eccentricity formulas (Eqs. 11-12) assume the foveation point is the viewport center; gaze drift re-labels which retinal zone each pixel actually falls on.
  • domain assumption Five-parameter logistic maps quality scores to MOS
    Eq. (18), following [33,34]. This is the standard IQA regression choice; it makes the PCC/RMSE numbers in Tables 5 and 7 depend on a monotone transform assumption.
  • domain assumption Five-degree linear blending belts hide zone boundaries
    Sec. 3: 'to prevent noticeable boundaries between low and high quality zones, belts with the width of 5 degrees... smoothly change using a linear function', following [12]. If the belts themselves are visible, the measured zone-weight differences are biased.
  • domain assumption Lens-equation geometry with F=62mm, S0=25mm, S2=10mm maps viewport pixels to eccentricity
    Eqs. (1)-(12) in Sec. 2.1; the constants are stated as approximate ('S0=25mm and S2=10mm respectively'). This mapping converts pixel distance to degrees and sets every zone boundary in the stimuli.

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Pith. "Pith review of Impacts of Retina-related Zones on Quality Perception of Omnidirectional Image." pith.science (2026). https://pith.science/paper/TOMKJUUW

@misc{pith2026190806239,
  author       = {Pith},
  title        = {Pith review of: Impacts of Retina-related Zones on Quality Perception of Omnidirectional Image},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TOMKJUUW}},
  note         = {Machine review of arXiv:1908.06239}
}
read the original abstract

Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the human quality perception of omnidirectional images, focusing on different zones surrounding the foveation point. For that purpose, an extensive subjective experiment is carried out to assess the perceptual quality of omnidirectional images with non-uniform quality. Through experimental results, the impacts of different zones are analyzed. Moreover, nineteen objective quality metrics, including foveal quality metrics, are evaluated using our database. It is quantitatively shown that the zones corresponding to the fovea and parafovea of human eyes are extremely important for quality perception, while the impacts of the other zones corresponding to the perifovea and periphery are small. Besides, the investigated metrics are found to be not effective enough to reflect the quality perceived by viewers.

Figures

Figures reproduced from arXiv: 1908.06239 by the authors.

Figure 1
Figure 1. illustrates a typical viewing geometry in VR sys￾tems. Assume that VP is the displayed viewport, the lens in the HMD produces a virtual viewport VP0 that is fur￾ther formed on the retina in the human eyes. Eccentricity e (degrees) is used to measure the angular distance from the central gaze direction to any point in the virtual viewport VP0 . Let F (units of length) be the focal length of the lens. S0, S1, and S2 (… view at source ↗
Figure 2
Figure 2. Density of photoreceptors in the retinal [23] [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Five regions of the retina The eccentricity e of point M0 in the virtual viewport VP0 is given by e(xM0, yM0) = tan−1  d 0 S3  [degrees]. (12) It should be noted that parameters of a point on the virtual viewport are what actually used in a foveal quality metric. Moreover, given the knowledge of the human visual system, points on the virtual viewport can be divided according to the regions of the retina. 2.2 Regio… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Eight omnidirectional images used in our experiment [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Boundaries of zones in viewports used in our experiment [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: 95% confidence intervals of MOS values of a stimulus is the average score of the valid participants. The 95% confidence intervals of the MOS values are shown in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Weights of zones for each source image [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Performances of objective quality metrics [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Scatter plots of the values of the foveal quality metrics versus the MOS values for image [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.