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You've Seen Enough: Quality-Constrained Image Coding for Machines
Khoa Pham-Dinh, Sanaz Nami, Hamed Rezazadegan Tavakoli, Moncef Gabbouj, Farhad Pakdaman
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Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 95%
Last extracted: 9/23/2026, 3:48:49 AM
Summary
The paper proposes a Quality-Constrained Image Coding for Machines (ICM) framework that jointly optimizes image compression and semantic segmentation. Unlike traditional methods that implicitly balance quality and task performance, this approach explicitly constrains the visual quality (PSNR) to a user-defined target using penalty functions (absolute and bilinear). This allows the codec to redirect coding capacity from perceptual fidelity to machine task performance, resulting in significant bitrate reductions (BD-rate improvements) for segmentation tasks on the Cityscapes dataset without degrading human-perceived quality.
Entities (10)
Relation Signals (7)
Quality-Constrained Image Coding for Machines → evaluatedon → Cityscapes
confidence 99% · Qualitative and quantitative evaluations on the Cityscapes semantic segmentation dataset
Quality-Constrained Image Coding for Machines → achievesimprovementin → BD-Rate
confidence 97% · Experimental results show that, under the quality constraint, the proposed method achieves a BD-rate of −22.82% over an unconstrained joint rate--distortion--task optimization
Quality-Constrained Image Coding for Machines → optimizesfor → mIoU
confidence 96% · We improve the compression ratio for segmentation... summarize rate–task efficiency with the Bjontegaard delta rate (BD-rate) on the mIoU axis
Quality-Constrained Image Coding for Machines → uses → Absolute Penalty
confidence 95% · We propose two penalty functions, an absolute function and a bilinear function...
Quality-Constrained Image Coding for Machines → uses → Bilinear Penalty
confidence 95% · We propose two penalty functions, an absolute function and a bilinear function... the bilinear penalty performs best throughout.
Mask2Former → usedas → Task Network
confidence 92% · The task network is Mask2Former [5] with a Swin-T backbone [22], pretrained on Cityscapes and kept frozen.
Cheng2020 → usedas → Codec
confidence 90% · The codec is the attention-based Cheng2020 model from CompressAI [4, 6] across its six quality levels.
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Abstract
Abstract:Visual data is increasingly consumed by machine-vision systems rather than by human observers. Image Coding for Machines (ICM) compresses images assuming the main observer is a computer vision application and that the human observer needs to inspect or validate the decisions. Inspired by just-noticeable distortion, which sets the quality to the just-acceptable level for human observers, we aim to cap the human-observed quality at a desired level, with the goal of using the remaining coding capacity to improve the machine performance. We recast joint compression-segmentation training as a constrained optimization problem in which the codec must meet a predefined acceptable target visual quality while a task term consumes the remaining coding capacity. We solve this by designing a penalty function to guide the quality to the desired target. We propose two penalty functions, an absolute function and a bilinear function, the latter applying a steeper slope once the target visual quality is exceeded. Experimental results show that, under the quality constraint, the proposed method achieves a BD-rate of $-22.82\%$ over an unconstrained joint rate--distortion--task optimization and $-29.81\%$ over a simple rate--distortion baseline, showcasing bitrate reduction with the same task performance. This is achieved while the codec also meets the target visual quality with a reasonable error and without adding any complexity overhead.
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- Source: https://arxiv.org/abs/2609.25108v1
- Canonical: https://arxiv.org/abs/2609.25108v1
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YOU’VE SEEN ENOUGH: QUALITY-CONSTRAINED IMAGE CODING FOR MACHINES Khoa Pham-Dinh1, Sanaz Nami1 Hamed Rezazadegan Tavakoli2, Moncef Gabbouj1, Farhad Pakdaman2 Abstract Visual data is increasingly consumed by machine-vision systems rather than by human observers. Image Coding for Machines (ICM) compresses images assuming the main observer is a computer vision application and that the human observer needs to inspect or validate the decisions. Inspired by just-noticeable distortion, which sets the quality to the just-acceptable level for human observers, we aim to cap the human-observed quality at a desired level, with the goal of using the remaining coding capacity to improve the machine performance. We recast joint compression-segmentation training as a constrained optimization problem in which the codec must meet a predefined acceptable target visual quality while a task term consumes the remaining coding capacity. We solve this by designing a penalty function to guide the quality to the desired target. We propose two penalty functions, an absolute function and a bilinear function, the latter applying a steeper slope once the target visual quality is exceeded. Experimental results show that, under the quality constraint, the proposed method achieves a BD-rate of −22.82%-22.82\% over an unconstrained joint rate–distortion–task optimization and −29.81%-29.81\% over a simple rate–distortion baseline, showcasing bitrate reduction with the same task performance. This is achieved while the codec also meets the target visual quality with a reasonable error and without adding any complexity overhead. Index Terms: image compression, image coding for machine, computer vision, segmentation †address: Tampere University1, Nokia Technologies2 1 Introduction Image and video data dominate global network traffic, yet a substantial part of modern visual content is never consumed by a human; it is processed directly by machine-vision systems for computer vision tasks such as object detection, classification, and semantic segmentation. Classical codecs such as AVC, HEVC, and VVC rely on handcrafted tools that exploit the human visual system. Most end-to-end learned image compression (LIC) methods replace the hand-crafted pipeline of classical codecs with neural analysis and synthesis networks, trained on a relaxed rate–distortion objective L=R+λDL=R+λ D [3, 24, 6, 16, 34, 18, 19, 26, 35]. Most of the legacy work in LIC is optimized for human vision, with a focus on metrics such as Peak Signal-to-Noise Ratio (PSNR) and Multiscale Structural Similarity (MS-SSIM). These metrics, while designed to preserve fidelity, correlate weakly with the accuracy of the vision models that ultimately process the decoded images, so bits are routinely spent on texture details that are task irrelevant, but task-critical structure may be lost during reconstruction. As a result, the need to design machine-centric codecs that preserve semantic information rather than pixel fidelity has emerged. In recent years, ICM has emerged as a promising research direction, driven by the increasing reliance on automated machine analysis for image interpretation. Machine-targeted compression algorithms aim to reduce the bitrate while maintaining high performance on machine tasks such as classification, detection, and segmentation. Representative work can be broadly classified into two main paradigms [8, 12], following the standardization efforts of the Moving Picture Experts Group (MPEG). The first, Feature Coding for Machines (FCM), targets machine-only consumption, where no human observes the decoded stream. These methods compress intermediate features rather than image pixels [11, 13]. The second paradigm, Video/Image Coding for Machines (VCM/ICM), operates in the pixel domain and jointly optimizes codecs for hybrid human-machine consumption, where the content must remain interpretable to a human who may inspect or supervise the decisions. This paradigm preserves a decodable image and trains the codec with an additional task loss for joint human-machine consumption. Recent standardization efforts in VCM/ICM have attracted considerable attention, encompassing the development of coding tools and support for diverse real-world use cases [14, 10, 2, 27]. While some works improve machine performance by pre- or post-processing in the image domain [30, 23], progress in VCM/ICM has largely stemmed from advances in adapting LIC models for machine consumption [20, 21, 7, 17, 1, 29]. We target an ICM setting similar to [20]. Unlike these methods, which tune the loss weighting to trade off quality and task performance, we constrain the distortion to an explicit target. Motivated by just-noticeable-difference (JND) studies, which indicate that a human observer is satisfied once reconstruction passes a “good-enough” quality [25], we argue that fidelity beyond that level wastes bits that could instead serve the task. In a joint objective optimization scheme, the balance between quality and task is governed only implicitly by using two Lagrangian multipliers; that is to say, the quality realized at a given bitrate is whatever the optimizer settles on, rather than being guided to a desired level. This raises the central question of this work: how to control a codec’s output quality while reallocating the learning capacity freed from perceptual fidelity toward the downstream task? We propose an optimization scheme with the target quality as an explicit objective. Training is formulated as a constrained optimization problem that minimizes the rate and task losses, subject to the quality (or rather distortion associated with the target quality) reaching a user-defined target. This is converted into an unconstrained objective with a first-order penalty on the distortion. By doing so, we hypothesize that the model will redirect capacity from optimizing for quality, to optimizing for task performance. We propose that, rather than continuing to optimize for quality once the target has been reached, the optimizer should penalize any further quality improvement beyond the target threshold. Our contributions are: • A quality-constrained rate–distortion–task formulation that enforces a target quality through a penalty term. We improve the compression ratio for segmentation, while meeting the target quality. • Qualitative and quantitative evaluations on the Cityscapes semantic segmentation dataset, including an in-depth analysis of the coding gain and task performance. 2 Related Work Early work on learned image compression by Toderici et al. [31] regulated the compression rate via the quantization step. Rozendaal et al. [28] constrained the distortion term rather than the bitrate. Their method enables targeting specific distortion values without extensive hyperparameter tuning, facilitating direct pointwise model comparisons by fixing distortion across models and examining only the resulting bitrate. The results demonstrate compression performance on par with the classical rate–distortion approach. Nowadays, the λ-based formulation in rate-distortion theory, R+λ×DR+λ× D, has become the prevailing approach in learned image compression. Despite its efficacy, the method still exhibits notable limitations: it is ill-suited for targeting specific operating points—that is, each image yields a slightly different bitrate or quality level, rendering rate-matching for image compression a laborious and imprecise process. A common strategy for solving a constrained optimization problem is to reformulate it as an unconstrained one. Analogous techniques have been employed to regulate the bitrate in learned image compression. The LIC approach [15] modifies the loss function by penalizing deviations from the target bitrate via an additional term: β⋅(Rt−RRt)2β· ( R_t-RR_t )^2, where RtR_t denotes the target rate. Xue et al. [33] predict bitrates for several predefined λ values using a lightweight hybrid convolution-transformer estimator, and fit a modified exponential R-λ model to map the target bitrate to its corresponding λ. Leveraging this predicted λ, the codec executes a single-pass encoding that accurately attains the desired bitrate. Xie et al. [32] propose MVRNet, a no-reference JRD prediction framework that generates a spatially-varying quantization map by identifying how much compression each region can tolerate without degrading detection accuracy. By allocating more bits to task-critical regions (e.g., low-confidence or unfamiliar objects) and fewer bits to redundant background or high-confidence areas, the resulting QP map preserves machine task accuracy at substantially lower bitrates. 3 Methodology 3.1 Framework The joint compression and task model couples an LIC codec, with trainable parameters θ, to a downstream task network with parameters ϕφ (Fig. 1). The codec comprises a main auto-encoder—an analysis transform gag_a and a synthesis transform gsg_s. An input image x is encoded to a quantized latent y y and decoded to a reconstruction x x, which is passed to the task network. During training we optimize only θ; the task network is frozen and provides the differentiable task loss, yet its gradients still propagate into the codec. Three losses are used: the rate R, defined as the negative log-likelihood of y y and z z under the entropy model; the distortion D, defined as D=MSE(x,x^)D=MSE(x, x); and the task loss T given as the pixel-wise cross-entropy of the segmentation prediction on x x. Figure 1: Joint ICM framework. The compression network is trained and the frozen segmentation network supplies the task loss. 3.2 Baselines The baseline is a pure rate–distortion-based learned codec, trained for human vision, whose reconstruction is fed to the task network only at inference. We also build a naive ICM method, the Joint Optimization (JO), which is the unconstrained joint model that adds the task loss during training. LBaseline=R+λ1D,LJO=R+λ1D+λ2TL_Baseline=R+ _1\,D, L_JO=R+ _1D+ _2T (1) 3.3 Quality-Constrained Formulation DtD_tλ1Dt _1D_tℒdistortionL_distortionP Figure 2: Penalty functions w.r.t. distortion. Blue shows absolute penalty λ1ℒdistortion _1L_distortion. The bilinear λ1(ℒdistortion,Target) _1P(L_distortion,D_Target) in red, applies a steeper slope after reaching the target. Rather than allowing the weighting to set the quality implicitly, we require the quality (in fact the distortion associated with the quality) to meet a user-defined target. The user specifies the target quality as a PSNR value PSNRtPSNR_t. This leads to the optimization in (2). minθR+λTs.t.PSNR(x,x^)=PSNRt _θ\;R+λ\,T .t. (x, x)=PSNR_t (2) Because PSNR is a monotonic transform of MSE, the target is specified as a target PSNR and converted to a distortion target: PSNR=10log10(2552/MSE)PSNR=10 _10(255^2/MSE), so Dt=2552/10PSNRt/10D_t=255^2/10^PSNR_t/10. Hence, the optimization problem is essentially written as minθR+λTs.t.D(x,x^)=Dt _θ\;R+λ\,T .t. (x, x)=D_t (3) Applying the penalty method in optimization to (3) converts the constrained problem into the unconstrained objective L=R+λ1(D(x,x^),Dt)+λ2T,L=R+ _1\,P\! (D(x, x),\,D_t )+ _2\,T, (4) where the penalty function P replaces the plain distortion term. The proposed penalties share a two-phase behavior: when the target quality has not been reached (D≥DtD≥ D_t) they reduce to standard rate–distortion (λ1D _1D). Once the target is reached (D<DtD<D_t) a penalty discourages spending further bits on visual quality. 3.4 Penalty Functions The absolute penalty is symmetric about the target: once the reconstruction exceeds the target quality it is penalized at the same rate at which sub-target distortion is rewarded, abs(D,Dt)=|D−Dt|+DtP_abs(D,D_t)=|D-D_t|+D_t (5) While it is effective at higher bitrates, we observed that it can underperform at low bitrates, where the penalty coefficient λ1 _1 is small (to meet the low bitrate) and the model still over-shoots the target. The bilinear penalty (Fig. 2) remedies this by applying a steeper slope λ1′ _1 once the target is passed, pushing the model back toward the target instead of over-spending bits on fidelity: bi(D,Dt)=D,D≥Dt,−λ1′λ1D+(1+λ1′λ1)Dt,D<Dt,P_bi(D,D_t)= casesD,&D≥ D_t,\\[2.0pt] - _1 _1D+ (1+ _1 _1 )D_t,&D<D_t, cases (6) While the optimization can be controlled via setting λ1′ _1 , λ1′= 2λ1 _1 =\,2 _1 has proved to be a suitable value in our experiments. This tunable slope makes the bilinear penalty more flexible, and as shown in experimental results, it performs better than the absolute penalty. 4 Results and Analysis 4.1 Experimental Setup Figure 3: Rate–performance curves on Cityscapes val. The proposed penalties improve mIoU at equal bitrate. We evaluate on Cityscapes (2975 training and 500 validation images, 19 classes), training on 256×256256× 256 patches with random cropping and validating on center crops. The codec is the attention-based Cheng2020 model from CompressAI [4, 6] across its six quality levels. The task network is Mask2Former [5] with a Swin-T backbone [22], pretrained on Cityscapes and kept frozen. Following the CompressAI protocol we use a dual optimizer (Adam at 10−410^-4 for the network and 10−310^-3 for the entropy auxiliary parameters) with a plateau learning-rate schedule with a patience of 1010 epochs. We report bits per pixel (bpp), PSNR, and mIoU computed with the standard Cityscapes script [9], and summarize rate–task efficiency with the Bjontegaard delta rate (BD-rate) on the mIoU axis, using Baseline and Joint Optimization as the references. For Baseline, λ ∈ \ 0.00180.0018, 0.00350.0035, 0.00670.0067, 0.0130.013, 0.0250.025, 0.4830.483 \. For the JO and the proposed methods, λ1 _1 ∈ \0.00180.0018, 0.00350.0035, 0.00670.0067, 0.0130.013, 0.0250.025, 0.4830.483\ and λ2 _2 ∈ \0.0010.001, 0.010.01\. The loss-term weights are kept constant throughout training. 4.2 Quantitative Results Figure 3 reports the rate–performance behavior: both proposed penalties lie above the two baselines at equal bitrate, with the largest margin in the low-to-mid bitrate range, and the bilinear penalty performs best throughout. Both penalties also outperform the standard codec VTM, used as the anchor in MPEG VCM [14]. Since the useful target quality grows with the bitrate, we further adopt a mixed-target schedule that sets a lower target in the low-bitrate region than in the high-bitrate region, as shown in Figure 3. Table 1 summarizes the BD-rate based on mIoU. At equal task performance, the bilinear penalty achieves a BD-rate of −22.82%-22.82\% over joint optimization and −29.81%-29.81\% over the Baseline. We also analyze each target individually, fixing the target quality across all bitrates. Table 2 reports the BD-rate by target PSNR level and confirms that a bitrate saving can be achieved for a wide range of target qualities. Figure 4 presents two examples of fixed quality: both mIoU curves show an advantage over the baselines, and the constraint is reflected in the PSNR curves being held near the target quality. Table 1: BD-Rate (%) and BD-mIoU relative to reference curves. Negative BD-Rate or positive BD-mIoU point to improvement. Proposed Reference BD-Rate (%) BD-mIoU(%) Bilinear Baseline -29.81 5.60 Joint Optimization -22.82 3.86 VCM VTM -45.78 10.75 Absolute Baseline -20.69 4.29 Joint Optimization -11.32 2.13 VCM VTM -38.44 10.29 Figure 4: Proposed Bilinear at PSNR targets of 29 and 33 dB versus Baseline and Joint Optimization. (a) mIoU vs bitrate: both proposed curves lie above the baselines, the 29 dB target highest. (b) PSNR vs bitrate: the constraint holds quality near each target (dotted), unlike the unconstrained baselines. Table 2: BD-Rate (%) per target-PSNR level on Cityscapes val (quality axis = mIoU). Each proposed curve uses the best λ2 _2 per quality point. BL = Baseline, JO = Joint Optimization. Proposed Bilinear Proposed Absolute Target (dB) vs BL vs JO vs BL vs JO 25 −14.6-14.6 −5.9-5.9 −20.8-20.8 −11.0-11.0 26 −15.3-15.3 −5.6-5.6 −19.2-19.2 −8.9-8.9 27 −21.1-21.1 −11.6-11.6 −20.5-20.5 −10.3-10.3 28 −21.1-21.1 −11.4-11.4 −23.5-23.5 −14.2-14.2 29 −30.8-30.8 −22.5-22.5 −22.4-22.4 −12.7-12.7 30 −28.3-28.3 −19.8-19.8 −18.7-18.7 −9.4-9.4 31 −24.5-24.5 −15.3-15.3 −10.5-10.5 −0.4-0.4 32 −17.6-17.6 −7.5-7.5 −18.9-18.9 −9.4-9.4 33 −20.0-20.0 −10.1-10.1 −8.7-8.7 +1.8+1.8 34 −14.2-14.2 −3.9-3.9 −8.2-8.2 +2.9+2.9 35 −12.8-12.8 −3.0-3.0 −7.5-7.5 +3.9+3.9 36 −15.9-15.9 −5.7-5.7 −6.9-6.9 +4.4+4.4 4.3 Analysis ((a)) ((b)) ((c)) ((d)) ((e)) ((f)) ((g)) ((h)) ((i)) ((j)) Figure 5: Analysis of lindau_000003_000019 at equal bitrate (≈ 0.030 bpp). Top: reconstructions; the proposed method trades ∼ 5 dB of PSNR for ++9.2 points of mIoU. Middle: per-pixel bit difference vs. Baseline and vs. JO (red = more bits, blue = fewer), and proposed-vs-JO segmentation (red/green = only JO/only proposed correct, gray = tie). Bottom: normalized highest-entropy latent channel. To understand why the constraint helps, we inspect and compare reconstructed images of Baseline, unconstrained Joint Optimization (JO), and the Proposed bilinear penalty, at a similar bitrate. We present one representative low-bitrate scene (lindau_000003_000019, ≈ 0.030 bpp) in this section. Analyzing low-bitrate examples helps to understand how the proposed method works under a tight resource constraint. Reconstruction quality. As observed in the top row of Fig. 5, all methods achieve a comparable visual quality at nearly identical bitrates (≈ 0.030 bpp). However, the proposed method deliberately loses 5 dB of PSNR (31.1 vs. 36.5 dB), which buys a 9.2-point improvement in segmentation performance (42.4% vs. 33.2%). Perceptually, the reconstruction remains acceptable: the proposed method spreads its extra error more uniformly over smooth, texture-poor areas (see the zoom insets), so the degradation lands where it matters least for segmentation while object boundaries stay sharp. Spatial bit allocation. Summing the latent bit cost over channels and mapping it back to image space shows where the constraint moves bits. The middle row of Fig. 5 shows the per-pixel bit difference of the Proposed codec relative to the Baseline and to JO: red marks added bits, blue marks savings. Bits move toward task-salient structure—vehicles, poles, and object boundaries—and away from large uniform regions such as road and sky. The segmentation error maps in the same row confirm the payoff: relative to JO, the Proposed codec recovers foreground regions that JO mislabels, consistent with its higher mIoU at equal rate. Latent representation. The normalized latent representation corresponds to the information transmitted through the bottleneck of the framework illustrated in Fig. 1: the greater the spatial regularity of the latent, the more predictable it is, and consequently the fewer bits are required to encode it. As shown in Fig. 5, in the low-bitrate regime the normalized latent of the proposed codec exhibits less smooth spatial structure than the baselines—a sign of a better-calibrated entropy model—and concentrates its bit budget on a few high-energy channels (bottom row of Fig. 5). It is further observed that the Baseline and JO codecs allocate an excessive number of bits to smooth, easily predictable regions such as road surfaces. Ranking channels by the divergence of their z-score distribution from a standard normal isolates the task-informative channels, which the proposed method activates more strongly. 5 Conclusion We presented a quality-constrained formulation for jointly training an image codec with a semantic segmentation task, which replaces the unconstrained distortion term with a penalty that enforces a target PSNR, letting the codec redirect the bits freed from perceptual fidelity toward the computer vision task. On Cityscapes the approach yields consistent BD-rate savings on the mIoU axis over both a pure rate–distortion baseline and an unconstrained joint optimization scheme, while holding the reconstruction close to the requested quality. Our analyses show that the codec shifts bits toward task-relevant regions. Two findings stand out: targeting a specific distortion level in coding for machines is feasible, and a moderate penalty enforcement improves task accuracy. 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