Simple Primary Colour Editing for Consumer Product Images

Han Gong1, Luwen Yu2, and Stephen Westland2

1School of Computing Sciences, University of East Anglia, UK2School of Design, University of Leeds, UK

AbstractWe present a simple primary colour editing methodfor consumer product images. We show that by us-ing colour correction and colour blending, we can au-tomate the pain-staking colour editing task and savetime for consumer colour preference researchers. Toimprove the colour harmony between the primarycolour and its complementary colours, our algorithmalso tunes the other colours in the image. Prelimi-nary experiment has shown some promising resultscompared with a state-of-the-art method and humanediting.

1 IntroductionResearch and data have been increasingly used tooptimise the design process [33]. Previous researchshows that product-colour appearance can affectconsumers’ purchase decisions while consumers’product-colour preferences vary from category tocategory [20, 34]. To understand consumer-productcolour preference, the standard marketing imageshave been manually recoloured using software suchas Adobe Photoshop [3] or GIMP [1]. The recolour-ing process requires researchers (or designers) tomanually adjust colours by picking colours andadopting non-binary per-layer masking. However,visible artefacts and incompatible backgroundcolours often remain even after very careful editing.We conclude four main requirements from colourpreference researchers:Minimum machine processing time. Users wouldnot prefer a slow processing speed as usually colourmodifications are applied to multiple products for atleast dozens of target colours.Minimum user manipulation time. A high demandof user interaction time would be undesirable.Methods that require multiple user strokes or manualselection of multiple colours would not be ideal. We

need a method that only requires a single primarycolour specification and takes care of the rest ofcolour processing automatically.Artefact resiliency. Artefacts, such as JPEG blocksand unnatural edges, are usually introduced afterre-colouring. It is expected to preserve all imagedetails except for the primary colour modification.Colour harmony preservation. The chosencolour for change may not fit the product’s existingcomplementary colours. In some cases, tuning ofcomplementary colours is desirable.

Our proposed method in this paper addresses theserequirements by providing an alternative design toolwhich is fully automatic. Existing studies suggestedthat colour manipulations offer the potential for soft-ware to generate recoloured images (target colourimages). More promising applications of automaticcolour manipulations will lead the trend of genera-tive design systems in colour and design [28, 33].There have been a number of methods for colourmanipulations such as colour transfer [23, 26, 27],colour hint propagation [4, 8–10, 19], or palette edit-ing [7, 24, 35]. However, none of the previous meth-ods is directly applicable to the primary colour edit-ing problem. Rapid digital workflows in practicewould also require automatic methods for evaluatingand/or comparing colours and designs [5].

In this paper, we propose a simple method whichautomates primary colour editing at an optimisedconsumption of user and machine processing timeand it preserves colour harmony to some extent. Ourmethod is based on the assumption that simulatingcolour change as a 2-D colour homography [13] (i.e.as a change of light) usually avoids image processingartefacts [13, 16] such as JPEG blocks, sharp edges,and colour combination conflicts. Our colour edit-ing pipeline is depicted in Figure 1 where the colourediting task is reformulated as a 2-D colour homogra-phy colour correction problem. Additionally, we may

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Input Image

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Colour Correction

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(A) Intensity Distribution Clustering And Altering in Lab Space

(C) Irrelevant Colour Change

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Figure 1: Primary colour editing pipeline. Given an input image, our method amends the product’s pri-mary colour according to a target colour in 3-4 steps: A) Colour intensities clusters in CIE L*a*b* colourspace [29] are computed (left: original distributions; right: primary colour altered distributions); B) ClusterRGB correspondences are used for estimating a colour correction matrix. Note that the L* channel intensitiesare also used for clustering but are not illustrated on the exemplar graphs; C) An alpha-blending processis applied to remove colour changes, which are less relevant to the primary colour change, from the colourcorrected image; D) Some residual colour artefacts can be optionally removed using a gradient preservationmethod ’regrain’ [26] (see the latter section for visualisation). The a*b* chromaticity gamut images are takenfrom Wikipedia [2].

apply a gradient preservation step to remove someresidual artefacts. Compared with the previous re-colouring methods, ours requires minimum user in-put and its design is relatively simple.

2 Related WorkOur work is relevant to the colour editing methods inthree categories: A) colour transfer; B) colour hintpropagation; C) palette-based colour editing.

2.1 Colour transferColour transfer is an image editing process which ad-justs the colours of a picture to match a target pic-ture’s colour theme. This research was started byReinhard et al. [27] and followed up by the oth-ers [23, 25, 26] recently. Most of these methods alignthe colour distributions in different colour spaces,

which usually involve statistics alignment [23, 25,27] or iterative distribution shifting [26].

2.2 Colour hint propagationSome methods require user hints, e.g. strokes, toguide recolouring of object surfaces. This directionof research was started by Levin et al. [19] wherethey colourise grey-scale images based on user colourstroke and solves for a large and sparse system oflinear equations. Their key assumption is that thecolours of neighbouring pixels with similar lumi-nance should have similar chromaticities. More re-cent methods [4, 9, 10] make use of masks, either softor hard, to assist re-colourisation. Their colour mod-ification model is based on a diagonal colour correc-tion matrix used for white balance, e.g. [15] with lim-its on the range of applicable colour changes. Someothers, e.g. [8], have used sparse coding/learning thatthe sparse set of colour samples provide an intrinsic

basis for an input image and the coding coefficientscapture the linear relationship between all pixels andthe samples. This branch of methods require heavyuser inputs and therefore not immediately useful forour problem.

2.3 Palette-based colour editingSome methods adopt colour intensity clustering, e.g.k-means++ algorithm [6], to initially generate acolour palette of the input image. After palette adjust-ments, different approaches were applied for manip-ulating colour changes. Zhang et al. [35] decomposethe colours of the image into a linear combination ofbasis colours before reconstructing a new image us-ing the linear coding coefficients. Chang et al. [7]adopt a monotonic luminance mapping and radial ba-sis functions (RBFs) for interpolating/mapping chro-maticities. This branch of methods are most close toour solution however none of them is optimised forthe particular task of rapid primary colour editing forconsumer product images.

2.4 Colour homographyOur solution is based on the colour homographycolour change model. The colour homography the-orem [11–13, 16] presents that chromaticities acrossa change in capture conditions (light color, shadingand imaging device) are a homography apart. Sup-pose that we map an RGB ρ to a corresponding RGI(red-green-intensity) c using a 3× 3 full-rank matrixC:

ρᵀC = cᵀ RGB

ᵀ 1 0 10 1 10 0 1

=

RG

R+G+B

ᵀ

(1)The r and g chromaticity coordinates are written asr = R/(R+G+B) , g = G/(R+G+B). Wetreat the right-hand-side of Equation 1 as a homoge-neous coordinate and we have c ∝

[r g 1

]ᵀ.

When the shading is fixed, it is known that acrossa change in illumination or a change in device, thecorresponding RGBs are approximately related by a3 × 3 linear transform M that ρᵀM = ρ′ᵀ whereρ′ is the corresponding RGBs under a second lightor captured by a different camera [21, 22]. We haveH = C−1MC which maps colours in RGI form be-tween illuminants. Due to different shading, the RGItriple under a second light is written as c′ᵀ = αcᵀH ,

where α denotes the unknown scaling. Without lossof generality we regard c as a homogeneous coor-dinate i.e. assume its third component is 1. Then,[r′ g′]ᵀ = H([r g]ᵀ) (rg chromaticity coordinatesare a homography H() apart). In this paper, we willmodel the major colour change initially as a colourhomography change but without considering the in-dividual scale differences between each RGB corre-spondences, i.e. a 3 × 3 linear transform of colourchange is applied.

3 Simple Primary Colour Edit-ing

Our algorithm starts with the simple observation thata simple 2-D colour homography model allows fora wider range of colour changes (as opposed to adiagonal colour correction matrix) and usually pro-duces fewer colour combination conflicts [13, 16]. InFigure 1, we overview the colour processing pipelinewhich consists of three major steps and one optionalstep: A) Clustering: The CIE L*a*b* [29] inten-sities of an input RGB image are clustered usingMeanShift [14]. The primary colour cluster is al-tered to match the target colour (see the red line) thatthe cluster centres form the before-and-after sparsecolour intensities correspondences; B) Colour correc-tion: The L*a*b* colour correspondences are con-verted to RGB space before being used to estimate a2-D colour homography matrix (without consideringscale differences); C) Irrelevant colour change sup-pression: a soft alpha-blending mask is computed tosuppress aggressive colour changes irrelevant to theprimary colour change; D) Gradient preservation (op-tional): a gradient preservation step can be applied toremove more residual artefacts. We also note that thecomputational cost can be reduced by using down-sampled thumbnail images for model parameter esti-mation. We provide the algorithm details in the fol-lowing sub-sections.

3.1 Intensity clustering and altering

To estimate a reliable colour change model, the firststep is to extract the predominant colours which bestcapture the input image’s colour theme. We adoptMeanShift [14] clustering to extract at most 5 pre-dominant colours (i.e. cluster centres) from the in-put image. The intention of not collecting too manycolours is to avoid noise and reduce computationalcost. The cluster number of 5 is only an empirical

value, e.g. 6 also works. Clearly, a fixed set of Mean-Shift parameters never guarantee a maximum num-ber of 5 colour clusters. We thus propose a sim-ple adaptive MeanShift clustering procedure whichgradually increases the initial small kernel bandwidthvalue as shown in Algorithm 1 where MeanShift is

Algorithm 1: Adaptive MeanShift clustering

1 w = 0.1, β = 1.5;2 repeat3 C = MeanShift(w,Alab);4 n = len(C);5 w = βw;6 until n > 5;

the MeanShift function with a flat kernel and band-width w, C is a n× 3 matrix of cluster centres (eachrow is a L*a*b* intensity vector), len counts thenumber of cluster centres n, β is a factor controllingthe kernel width growth rate in each iteration.

Given the obtained predominant colours, we con-struct the sparse colour correspondences to be sup-plied for colour change model estimation. Since weaim to only change the one primary colour if possi-ble, the remaining of target predominant colours arekept the same as the original predominant colours ex-cept that the only primary colour is modified as thetarget primary colour. Through this, we construct atarget predominant colour set denoted as D (see alsoFigure 1 (A) for illustration).

3.2 Colour Homography colour change

Given the source and target colour sets C and D, wemake use of a simple 2-D colour homography matrixto achieve primary colour change while minimisingcolour artefacts. A full colour homography changeis an optimised chromaticity mapping in RGB space.However, since the brightness of colour matters inthis application, we omit the shading factor α andonly estimate a 3× 3 linear matrix transform (whichis still a homography matrix) using weighted least-squares as the follows:

M = (CᵀWC + kI3×3)−1CᵀWD (2)

where k = 10−3 is a regularisation term, W is a di-agonal matrix whose diagonal elements are the as-sociated normalised weights of all the predominantcolours (i.e. cluster centre sizes), I3×3 is a 3×3 iden-tity matrix. Denoting the ’flatten’ RGB intensities of

the input image as a N × 3 matrix A (N is the num-ber of pixels), we can compute its primary-colour-changed RGB intensities as B = AM . An interme-diate processed example can be found in Figure 1 (B).

3.3 Irrelevant colour change suppres-sion

Some of the colour changes after the 3 × 3 lineartransform may look aggressive, e.g. the pink ring ofthe ’Tide’ logo in Figure 1 (B). We adopt an alpha-blending procedure to address this as the follows:

B′ = (1− diag(d))B + diag(d)A (3)

where B′ is the modified RGB colour output, d isan N-vector denoting per-pixel scaling factors (in therange of [0, 1]) and diag() places an N-vector alongthe diagonal of an N × N diagonal matrix. Our in-tuition is to smoothly reduce the impact of the colourchanges that are irrelevant to the primary colour andcontrol this by d. We measure the irrelevance bythe a*b* chromaticity difference ∆E between eachcolour (row) in B′ and the target primary colour:

∆E =√

∆a∗2 + ∆b∗2 (4)

where ∆a∗ and ∆b∗ are the errors in a*b* channels.A higher ∆E indicates a higher degree of irrelevancebut this value can be sometimes too big. Thus, wefurther cap and normalise ∆E as ∆E′:

∆E′ =

{1 ∆E > ∆Emax

∆E/∆Emax Otherwise (5)

where ∆Emax is an upper threshold value. The in-dividual ∆E′ is assigned as the corresponding ele-ment of d. The processing result can be sensitiveto ∆Emax and thus ∆Emax must be carefully cho-sen. An exemplar visualisation of d in its image gridform is shown in Figure 2 (A). Aiming at obtaininga blending result which preserves the edge details ofthe original image, we look for the optimum ∆Emax

which minimises Equation 6.

min∆Emax

Σc∈{a∗,b∗}entropy(|edge(IB′,c)−edge(IA,c)|)(6)

where || is the operator to output per element abso-lute value of a matrix, c indicates an intensity channelof a* or b*, IB′,∗ and IA,∗ indicate the grid imagesof the ’unflatten’ intensity matrix B′ and A respec-tively, edge is a binary edge detector using the Sobelapproximation [32] to the derivative (without edge-thinning), entropy is a function which measures the

amount of information – entropy [31] – as defined inEquation 7.

entropy(p) = −∑i

pi log pi (7)

where p is a normalised input vector (summed upto 1) which, in our case, is a ’flattened’ error-of-edge image (e.g. Figure 2 (C)), i is an element index.When the entropy of the error of two edge images islow, it indicates a higher similarity of edge featuresbetween two intensity images. However, we do nothave a closed-form solution for its global minima.In practice, a suitable local minima in a reasonablerange usually serves the purpose. We thus propose abrutal search for a local minima solution of ∆Emax

in the range of [10, 210] with an interval precision of20. A visualised example of d and its plot of ∆Emax

search are shown in Figure 2.

Input Image

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(A) Colour Change Irrelevance Map

(B) Entropy plot

(C) Edge Differences of a* (thumbnail)

10 30 50 70 90 110 130 150 170 190

0.64

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70 50

Figure 2: Visualisations of the alpha blending. A)Visualised alpha blending mask used in the irrelevantcolour change suppression step. B) The associatedentropy plot (horizontal ticks: ∆Emax). C) Visuali-sation of binary edge difference in channel a* when∆Emax = 70 or ∆Emax = 50 (more different). Thebottom-right number indicates the value of ∆Emax.See also Figure 1 (A) for the input image and the tar-get primary colour.

3.4 Artefact cleansingAs the previous alpha blending step has attempted theminimisation of edge artefacts, mostly users can getan artefact-free output image. However, for some rarecases, we also adopt an optional artefact cleansingstep called ’regrain’ which was first proposed in [26].It provides strong gradient preservation but also hasside effects which may cause minor undesired blursalong edges. Please refer to the cited paper for the al-gorithm details. Figure 3 shows an example where

this optional step improves the result by removingsome JPEG block artefacts.

Input Image

Input Image

70 50

Output w/o Regrain Output with Regrain

Target

Colour

Figure 3: Example of the ’regrain’ [26] artefactcleansing.

3.5 AccelerationOur colour manipulation pipeline requires the solu-tion of 10 key model parameters, namely M and∆Emax. Using full-resolution images is not neces-sary and we therefore adopt thumbnail images (32×32) for solving for M and ∆Emax and apply the es-timated parameters to a full-resolution input image toget a full-resolution output.

4 EvaluationIn this section, we present the result comparison andsome useful discussions about our method’s practicaluse-cases.

4.1 ResultsWe compare our method with a state-of-the-artpalette-based re-colouring method [7] and the manu-ally edited results produced by a professional colourpreference researcher. Figure 4 shows some visualresult comparisons. We found that our outputs aremostly comparable to the manually edited resultswhich take 2-5 minutes’ labour time per image. Mostof the human labour time is spent on masking theimage (for primary colour pixels). Once the maskis completed, the remaining recolouring time takesabout 1 minute. All the results in Figure 4 have beenproduced without the ’regrain’ step enabled. Ourmethod also has some failure cases as shown in Fig-ure 5. These failures were caused by the initial stepof colour clustering. When the input image only hasone colour, the MeanShift clustering algorithm canmishandle the primary colour extraction. Loweringthe maximum cluster number (i.e. 5 in Algorithm 1)

Input Chang 2015 [7] Ours Human

Figure 4: Result visual comparison. The target colours are shown at the top right of the input images.The label ’Human’ refers to the column of results manually generated by a professional colour preferenceresearcher.

can resolve this issue. That said, we could providethis as an optional parameter for users.

Input Image

A) Partial colour replacement70 50

B) Incorrect colour replaced

Figure 5: Failure cases. The input images are shownat the bottom right and the target primary colours areshown at the bottom left. A) Only a part of the pri-mary colour was replaced. B) An incorrect primarycolour was picked and replaced.

Our method provides practical editing efficiencywithout user interventions. Using the thumbnail ac-celeration trick, our unoptimised MATLAB imple-mentation (without the regrain step) takes about 1sto process an 1.2 mega-pixel image on a MacBookPro 2015 laptop (2.5 GHz Quad-Core Intel Core i7CPU).

4.2 Discussions’Work to forecast’ has suggested the use of colourto forecast consumer demand or resource-saving lev-els [30]. Colour has also been suggested as oneof the most powerful visual elements in packaging.Thus, choosing an appropriate colour for the designof packaging or product can significantly affect con-sumer decision-making [18]. This work could beapplied as a product-colour predictor for studyingproduct-packaging-colour in consumer purchase be-haviour. Or, as an image generation tool, it helps de-signers/researchers preview the multi-colour optionsof a product image.

We also acknowledge that more rigorous user ex-periments in controlled lighting/display conditionscould still be possibly carried out after the UK Covid-19 lockdown [17]. We, therefore, commit to provid-ing our source code to the research community in thehope that its evaluation and more potential use-casescan be further driven by the other cross-disciplinecommunities.

5 ConclusionIn this paper, we present a simple product re-colouring method for assisting consumer colour pref-

erence research. We show that by using a colourmanipulation pipeline, we can automate this primarycolour editing task for consumer colour preferenceresearchers. The complementary colours in the prod-uct image will also be adjusted to potentially makethe primary colour fits better. Future work is requiredto explore more of its use-cases and strengthen itsartefact resiliency.

6 AcknowledgmentWe also thank Dr Qianqian Pan from the Universityof Leeds for her useful discussions.

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