A computer-implemented method comprising: determining, by a computing system, a saliency map of a frame of a content item based at least in part on a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items; determining, by the computing system, a barrel projection representation for the frame; and applying, by the computing system, a view-based projection to the barrel projection representation for the frame, wherein the view-based projection enhances a quality in terms of pixel-density in which a region of at least one salient point of interest in the saliency map is presented; wherein the frame is represented as a barrel layout comprising a top portion, a bottom portion and a middle portion of the frame, wherein the top and bottom portion are formatted in an equirectangular layout, wherein for applying the view-based projection a first region in the middle portion is stretched horizontally to increase pixel density, wherein one or more second regions in the middle portion are shortened horizontally to decrease pixel density in those regions, the method furthermore comprising: determining a vector-based representation of the saliency map of the frame by segmenting the saliency map into a set of yaw bins and a set of pitch bins, wherein the set of yaw bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees, and wherein the set of pitch bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees; determining a net vector and normalized magnitude by adding the corresponding vectors and magnitudes for each bin in the vector based representation of the saliency map, wherein the net vector and normalized magnitude reflect an amount of offset that is expected from a viewer when viewing the frame; and determining an offset for the frame based on said net vector and normalized magnitude; and increasing a pixel density for a region corresponding to said first point of interest while decreasing a pixel density for a region corresponding to a second point of interest as indicated by the offset. determining, by a computing system, a saliency map of a frame of a content item based at least in part on a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items; determining, by the computing system, a barrel projection representation for the frame; and applying, by the computing system, a view-based projection to the barrel projection representation for the frame, wherein the view-based projection enhances a quality in terms of pixel-density in which a region of at least one salient point of interest in the saliency map is presented; wherein the frame is represented as a barrel layout comprising a top portion, a bottom portion and a middle portion of the frame, wherein the top and bottom portion are formatted in an equirectangular layout, wherein for applying the view-based projection a first region in the middle portion is stretched horizontally to increase pixel density, wherein one or more second regions in the middle portion are shortened horizontally to decrease pixel density in those regions, the method furthermore comprising: determining a vector-based representation of the saliency map of the frame by segmenting the saliency map into a set of yaw bins and a set of pitch bins, wherein the set of yaw bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees, and wherein the set of pitch bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees; determining a net vector and normalized magnitude by adding the corresponding vectors and magnitudes for each bin in the vector based representation of the saliency map, wherein the net vector and normalized magnitude reflect an amount of offset that is expected from a viewer when viewing the frame; and determining an offset for the frame based on said net vector and normalized magnitude; and determining a vector-based representation of the saliency map of the frame by segmenting the saliency map into a set of yaw bins and a set of pitch bins, wherein the set of yaw bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees, and wherein the set of pitch bins is associated with a pre-defined vector direction that ranges from negative 180 degrees to positive 180 degrees; determining a net vector and normalized magnitude by adding the corresponding vectors and magnitudes for each bin in the vector based representation of the saliency map, wherein the net vector and normalized magnitude reflect an amount of offset that is expected from a viewer when viewing the frame; and determining an offset for the frame based on said net vector and normalized magnitude; and increasing a pixel density for a region corresponding to said first point of interest while decreasing a pixel density for a region corresponding to a second point of interest as indicated by the offset.