In　this　paper,　we　investigate　into　the　problem　of　image　quality　assessment　(IQA)　and　enhancement　via　machine　learning.　This　issue　has　long　attracted　a　wide　range　of　attention　in　computational　intelligence　and　image　processing　communities,　since,　for　many　practical　applications,　e.g.,　object　detection　and　recognition,　raw　images　are　usually　needed　to　be　appropriately　enhanced　to　raise　the　visual　quality　(e.g.,　visibility　and　contrast).　In　fact,　proper　enhancement　can　noticeably　improve　the　quality　of　input　images,　even　better　than　originally　captured　images,　which　are　generally　thought　to　be　of　the　best　quality.　In　this　paper,　we　present　two　most　important　contributions.　The　first　contribution　is　to　develop　a　new　no-reference　(NR)　IQA　model.　Given　an　image,　our　quality　measure　first　extracts　17　features　through　analysis　of　contrast,　sharpness,　brightness　and　more,　and　then　yields　a　measure　of　visual　quality　using　a　regression　module,　which　is　learned　with　big-data　training　samples　that　are　much　bigger　than　the　size　of　relevant　image　data　sets.　The　results　of　experiments　on　nine　data　sets　validate　the　superiority　and　efficiency　of　our　blind　metric　compared　with　typical　state-of-the-art　full-reference,　reduced-reference　and　NA　IQA　methods.　The　second　contribution　is　that　a　robust　image　enhancement　framework　is　established　based　on　quality　optimization.　For　an　input　image,　by　the　guidance　of　the　proposed　NR-IQA　measure,　we　conduct　histogram　modification　to　successively　rectify　image　brightness　and　contrast　to　a　proper　level.　Thorough　tests　demonstrate　that　our　framework　can　well　enhance　natural　images,　low-contrast　images,　low-light　images,　and　dehazed　images.　The　source　code　will　be　released　at　https://sites.google.com/site/guke198701/publications.