Optimizing and Assessing the Quality of E-Commerce Product Images Using Deep Learning Techniques

With the rapid growth of e-commerce, online shopping has become an indispensable part of daily life. Product images serve as a crucial medium for consumers to understand the products, and their quality directly influences purchasing decisions. However, due to limitations in photography equipment, techniques, and image processing methods, a wide range of image quality exists across e-commerce platforms. High-quality product images not only accurately convey product information but also enhance consumer shopping experience and trust. Therefore, researching methods for assessing and optimizing ecommerce product image quality is of significant practical importance. Existing image quality assessment and optimization methods often suffer from subjectivity, inadequate detail enhancement, and inability to address multiple types of distortion simultaneously. This paper focuses on two main areas: (1) a quality assessment model for e-commerce product images based on content and distortion retrieval, and (2) an image enhancement network utilizing the Laplacian operator and wavelet transform. Through this research, the paper aims to develop an efficient and accurate system for assessing and optimizing product image quality, providing e-commerce platforms with effective image quality management solutions and offering new technical insights for the field of image processing.