Classification of natural-scene images affected by adverse effects into their respective challenge type is a challenging problem. The state-of-the-art usually focuses on developing a model that extracts invariant features to address the above challenges. However, this direction is ineffective and inefficient for solving industry-oriented problems. This is because industry-related problems are specific and have a limited dataset and complexity. Therefore, this work introduces a new multi-modal approach called data-centric, which focuses on the classification of images of challenges (complexities), namely, quality degradation, illumination effects, and contrast issues. Once the complexity is known, the existing method can be used to achieve the expected results without proposing a new model. Our work proposes a dual ResNet50 for extracting spatial-domain and discrete wavelet-domain based features, which outputs image features. To extract the features that assess the quality of the images, the proposed work introduces a dual-CLIP-based text encoder by considering class labels as input. Therefore, the proposed model leverages image and textual features for the classification. The approach integrates image features in both spatial and wavelet domains, along with the textual context of the image quality, such as defect class labels. To evaluate the proposed method, the classification rate is estimated on our dataset and compared with the state-of-the-art methods.