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arxiv: 2604.08047 · v1 · submitted 2026-04-09 · 📡 eess.IV · cs.MM

A H.265/HEVC Fine-Grained ROI Video Encryption Algorithm Based on Coding Unit and Prompt Segmentation

Xiang Zhang , Haoyan Lu , Ziqiang Li , Ziwen He , Zhenshan Tan , Fei Peng , Zhangjie Fu This is my paper

Pith reviewed 2026-05-10 18:07 UTC · model grok-4.3

classification 📡 eess.IV cs.MM
keywords ROI encryptionHEVCH.265coding unitprompt segmentationselective encryptiondiffusion isolationPCM mode
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0 comments X

The pith

H.265 video encryption can now isolate regions of interest at the exact 8x8 coding-unit level instead of coarse Tiles.

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

The paper aims to replace Tile-based ROI encryption in HEVC with a method that maps arbitrary regions directly onto the smallest coding units. It introduces prompt segmentation to align ROIs with 8x8 boundaries, then selectively alters syntax elements inside those units while forcing PCM mode and motion-vector restrictions on neighboring blocks to stop prediction from spreading the encryption. If these steps work, sensitive areas such as medical or surveillance footage can be protected at pixel-level precision without visible artifacts outside the intended zone or large bitrate overhead. The authors test the approach on standard sequences and report that the encrypted regions match the target shapes, pixels inside them become unintelligible, and diffusion is eliminated.

Core claim

By combining prompt segmentation for exact 8x8 CU mapping, multi-syntax-element distortion inside the mapped units, and PCM-plus-MV-restriction isolation on affected blocks, the algorithm achieves fine-grained ROI encryption at the minimum coding-unit size while removing the diffusion artifacts that normally accompany HEVC prediction.

What carries the argument

Prompt segmentation that maps ROIs onto 8x8 coding units, followed by PCM mode and motion-vector restriction to isolate encryption diffusion.

If this is right

  • ROI boundaries can be defined at 8x8 precision rather than the larger Tile granularity used in prior HEVC encryption schemes.
  • Selective alteration of syntax elements inside the mapped CUs produces unintelligible pixels inside the target region.
  • Forcing PCM mode and restricting motion vectors on affected units prevents encryption from propagating through inter-frame prediction.
  • The method avoids the over-encryption of non-sensitive areas that occurs when Tiles are used as the encryption unit.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same CU-level mapping could be combined with automated object detectors to produce real-time selective protection in live medical or drone video feeds.
  • If the PCM restriction increases bitrate on high-motion content, a fallback to lighter syntax changes on non-key frames might be needed to keep the scheme practical.
  • Because the encryption operates only on syntax elements, standard HEVC decoders without the key will simply display heavily distorted ROIs while the rest of the frame remains intact.

Load-bearing premise

Prompt segmentation will always align any chosen ROI exactly to 8x8 coding-unit boundaries without leftover errors or over-segmentation, and the PCM/MV restrictions will fully contain encryption effects without creating new visual artifacts or unacceptable bitrate increases.

What would settle it

Run the algorithm on a test sequence containing an irregular ROI that crosses coding-unit boundaries; if the encrypted output shows either visible leakage outside the stated ROI or residual prediction artifacts inside neighboring blocks, the isolation claim fails.

Figures

Figures reproduced from arXiv: 2604.08047 by Fei Peng, Haoyan Lu, Xiang Zhang, Zhangjie Fu, Zhenshan Tan, Ziqiang Li, Ziwen He.

Figure 1
Figure 1. Figure 1: Comparison between the existing methods and the proposed [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed scheme is divided into three modules: ROI mapping based on prompt segmentation, ROI selective encryption based on multiple syntax elements, and diffusion isolation based on PCM mode and MV restriction. Among them, the ROI mapping consists of object detection, prompt segmentation and CU mapping. The ROI masks generated by this module serve as input for the ROI selective encryption module. Then,… view at source ↗
Figure 3
Figure 3. Figure 3: Reference pixel position for intra prediction [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: CU scanning order in H.265/HEVC In summary, under this scanning strategy, when encoding the CU above or to the left of the ROI, the ROI has not yet been encoded and therefore will not be affected. However, After the ROI is encoded and encrypted, the neighboring CUs in the right, lower, and diagonal directions may reference the reconstructed pixels of ROI during the prediction process ac￾cording to the refe… view at source ↗
Figure 5
Figure 5. Figure 5: Results after detection and segmentation. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visual examples of the proposed scheme. C. Performance Analysis We compare three Full-frame encryption schemes proposed by Sheng et al. [25], Fu et al. [26], Tie et al. [27] and the ROI encryption scheme proposed by Zhang et al. [5], and Taha et al. [15]. Due to the unstable encryption performance of each frame and the different number of frames in each test sequence, in order to ensure the consistency of … view at source ↗
Figure 7
Figure 7. Figure 7: Visual ablation study of diffusion isolation. From left to [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual comparison of ROI encryption results on four test sequences. Each row corresponds to one sequence: the original frame, the results encrypted by [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
read the original abstract

ROI (Region of Interest) video selective encryption based on H.265/HEVC is a technology that protects the sensitive regions of videos by perturbing the syntax elements associated with target areas. However, existing methods typically adopt Tile (with a relatively large size) as the minimum encryption unit, which suffers from problems such as inaccurate encryption regions and low encryption precision. This low-precision encryption makes them difficult to apply in sensitive fields such as medicine, military, and remote sensing. In order to address the aforementioned problem, this paper proposes a fine-grained ROI video selective encryption algorithm based on Coding Units (CUs) and prompt segmentation. First, to achieve a more precise ROI acquisition, we present a novel ROI mapping approach based on prompt segmentation. This approach enables precise mapping of ROIs to small $8\times8$ CU levels, significantly enhancing the precision of encrypted regions. Second, we propose a selective encryption scheme based on multiple syntax elements, which distorts syntax elements within high-precision ROI to effectively safeguard ROI security. Finally, we design a diffusion isolation based on Pulse Code Modulation (PCM) mode and MV restriction, applying PCM mode and MV restriction strategy to the affected CU to address encryption diffusion during prediction. The above three strategies break the inherent mechanism of using Tiles in existing ROI encryption and push the fine-grained level of ROI video encryption to the minimum $8\times8$ CU precision. The experimental results demonstrate that the proposed algorithm can accurately segment ROI regions, effectively perturb pixels within these regions, and eliminate the diffusion artifacts introduced by encryption. The method exhibits great potential for application in medical imaging, military surveillance, and remote areas.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a fine-grained ROI selective encryption algorithm for H.265/HEVC that replaces tile-based units with coding-unit (CU) granularity. It introduces (1) a prompt-segmentation mapping to align ROIs with the smallest 8×8 CUs, (2) selective encryption of multiple syntax elements inside those CUs, and (3) diffusion isolation by forcing PCM mode and motion-vector restrictions on affected CUs. The authors claim these three changes break the tile mechanism, achieve minimum-CU precision, and that experiments confirm accurate segmentation, effective pixel perturbation, and elimination of diffusion artifacts, with potential use in medical, military, and remote-sensing video.

Significance. If the boundary-alignment and diffusion-isolation claims are quantitatively verified, the work would meaningfully advance selective encryption precision in HEVC from tile scale to the native 8×8 CU scale. This is relevant for applications that require protecting only small sensitive regions without encrypting large tiles or entire frames. The integration of prompt segmentation with HEVC’s quadtree structure is a novel engineering step, though its practical value hinges on whether pixel-level masks can be forced onto rate-distortion-driven CU boundaries without leakage or overhead.

major comments (2)
  1. Abstract: the central claim that the algorithm 'can accurately segment ROI regions, effectively perturb pixels within these regions, and eliminate the diffusion artifacts' is asserted without any quantitative metrics (boundary IoU, pixel-level encryption error, bitrate overhead, or comparison against tile baselines). Because the 8×8-precision and 'no diffusion' assertions rest entirely on these unshown results, the absence of numbers, datasets, and error analysis is load-bearing.
  2. Prompt-segmentation and diffusion-isolation description: the method assumes prompt segmentation produces masks that align exactly with HEVC’s content-adaptive 8×8 CUs and that PCM+MV restriction fully severs all intra/inter prediction dependencies. No analysis is given of fractional CU overlaps, quadtree boundary errors, or the resulting bitrate penalty, which directly affects whether the 'minimum 8×8 CU precision' and 'elimination of diffusion artifacts' claims hold.
minor comments (2)
  1. The abstract and method overview do not name the specific prompt model, prompt-engineering details, or HEVC reference software version used, hindering reproducibility.
  2. No table or figure is referenced that would allow a reader to inspect the claimed segmentation masks or encrypted-frame visuals against ground-truth ROIs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas where quantitative support and analysis can be strengthened. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [—] Abstract: the central claim that the algorithm 'can accurately segment ROI regions, effectively perturb pixels within these regions, and eliminate the diffusion artifacts' is asserted without any quantitative metrics (boundary IoU, pixel-level encryption error, bitrate overhead, or comparison against tile baselines). Because the 8×8-precision and 'no diffusion' assertions rest entirely on these unshown results, the absence of numbers, datasets, and error analysis is load-bearing.

    Authors: We agree that the abstract would be strengthened by including specific quantitative metrics. The full experimental section reports results on segmentation accuracy, pixel perturbation, and diffusion elimination using relevant video datasets, but these are not summarized numerically in the abstract. In the revised manuscript we will update the abstract to incorporate key metrics such as boundary IoU for ROI-CU alignment, pixel-level encryption error rates, bitrate overhead percentages, and comparisons against tile-based baselines, along with explicit dataset references. revision: yes

  2. Referee: [—] Prompt-segmentation and diffusion-isolation description: the method assumes prompt segmentation produces masks that align exactly with HEVC’s content-adaptive 8×8 CUs and that PCM+MV restriction fully severs all intra/inter prediction dependencies. No analysis is given of fractional CU overlaps, quadtree boundary errors, or the resulting bitrate penalty, which directly affects whether the 'minimum 8×8 CU precision' and 'elimination of diffusion artifacts' claims hold.

    Authors: The prompt segmentation is constructed to map ROIs onto the smallest 8×8 CUs by operating at the quadtree leaf level, and the PCM mode plus MV restrictions are intended to break prediction chains. We acknowledge that the current description does not provide explicit analysis of fractional overlaps, boundary errors, or bitrate penalties. In the revision we will add a focused subsection that quantifies these factors, including measured overlap rates, boundary error statistics, and the bitrate overhead attributable to the isolation techniques, thereby directly supporting the precision and no-diffusion claims. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic construction without self-referential derivation

full rationale

The paper presents a three-part algorithmic construction (prompt-based ROI-to-CU mapping, syntax-element selective encryption, and PCM/MV diffusion isolation) that is claimed to achieve 8x8 precision and eliminate artifacts. No equations, fitted parameters, or predictions appear; the central claims are design assertions whose validity is asserted via experiment rather than reduced to prior inputs by definition or self-citation. The provided abstract and context contain no load-bearing self-citations or renamings that would trigger any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated. The method implicitly assumes standard HEVC syntax-element semantics and the existence of a working prompt-segmentation model, both treated as external inputs.

pith-pipeline@v0.9.0 · 5622 in / 1328 out tokens · 41073 ms · 2026-05-10T18:07:40.922467+00:00 · methodology

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Reference graph

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