Computer implemented classification method to classify quench patterns of heat-treated uncoated or coated mineral glasses; wherein said method takes as input a set of optical retardation image maps ORIM of heat-treated coated mineral glasses, a set of optical visibility scores OVS for quench patterns of said heat-treated uncoated or coated mineral glasses; and a colour sensitivity factor σ of the heat-treated uncoated or coated mineral glasses; wherein said optical visibility scores OVS are assessed by human observers according to an arbitrary scale for the quench patterns of said heat-treated uncoated or coated mineral glasses under given lighting and observation conditions; wherein said colour sensitivity factor σ represents the regions-to-regions colour difference gradient of said heat-treated uncoated or coated mineral glasses from their anisotropy; wherein said method provides as output a set of optimized values SOV for each component of an image processing pipeline IPP to be applied on said optical retardation image-maps ORIM; and wherein said method comprises the following steps: a) defining a frequency filter FF with a neighbourhood size FFs as a first component of said image processing pipeline IPP; b) defining a contrast filter CF with a neighbourhood size CFs as a second component of said image processing pipeline IPP; c) defining a root mean operation RMO with an index α over the image contrast as a third component of said image processing pipeline IPP; d) defining a colour correction function CCF as a fourth component of said image processing pipeline IPP, said colour correction function is a multiplication of the colour sensitivity factor σ by a trigonometric function of a root mean operation RMO; e) performing a regression operation between said colour correction function CCF and the set of optical visibility scores OVS through the varying parameters FFs, CFs, α while the image processing pipeline IPP is applied over the set of optical retardation image maps ORIM, the obtained values for said varying parameters being the set of optimized values SOV for each component of said image processing pipeline IPP. wherein said optical visibility scores OVS are assessed by human observers according to an arbitrary scale for the quench patterns of said heat-treated uncoated or coated mineral glasses under given lighting and observation conditions; wherein said colour sensitivity factor σ represents the regions-to-regions colour difference gradient of said heat-treated uncoated or coated mineral glasses from their anisotropy; wherein said method provides as output a set of optimized values SOV for each component of an image processing pipeline IPP to be applied on said optical retardation image-maps ORIM; and wherein said method comprises the following steps: a) defining a frequency filter FF with a neighbourhood size FFs as a first component of said image processing pipeline IPP; b) defining a contrast filter CF with a neighbourhood size CFs as a second component of said image processing pipeline IPP; c) defining a root mean operation RMO with an index α over the image contrast as a third component of said image processing pipeline IPP; d) defining a colour correction function CCF as a fourth component of said image processing pipeline IPP, said colour correction function is a multiplication of the colour sensitivity factor σ by a trigonometric function of a root mean operation RMO; e) performing a regression operation between said colour correction function CCF and the set of optical visibility scores OVS through the varying parameters FFs, CFs, α while the image processing pipeline IPP is applied over the set of optical retardation image maps ORIM, the obtained values for said varying parameters being the set of optimized values SOV for each component of said image processing pipeline IPP. a) defining a frequency filter FF with a neighbourhood size FFs as a first component of said image processing pipeline IPP; b) defining a contrast filter CF with a neighbourhood size CFs as a second component of said image processing pipeline IPP; c) defining a root mean operation RMO with an index α over the image contrast as a third component of said image processing pipeline IPP; d) defining a colour correction function CCF as a fourth component of said image processing pipeline IPP, said colour correction function is a multiplication of the colour sensitivity factor σ by a trigonometric function of a root mean operation RMO; e) performing a regression operation between said colour correction function CCF and the set of optical visibility scores OVS through the varying parameters FFs, CFs, α while the image processing pipeline IPP is applied over the set of optical retardation image maps ORIM, the obtained values for said varying parameters being the set of optimized values SOV for each component of said image processing pipeline IPP.