Another way to look at our threshold matrix is as a kind of probability matrix. Instead of offsetting the input pixel by the value given in the threshold matrix, we can instead use the value to sample from the cumulative probability of possible candidate colours, where each colour is assigned a probability or weight . Each colour’s weight represents it’s proportional contribution to the input colour. Colours with greater weight are then more likely to be picked for a given pixel and vice-versa, such that the local average for a given region should converge to that of the original input value. We can call this the N-candidate approach to palette dithering.
"It was a lot of trial and error."
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We know where it broke, but we can’t see why. Was it a race condition? Did a database read return stale data that has since been overwritten? To find the cause, we have to mentally reconstruct the state of the world as it existed milliseconds before the crash. Welcome to debugging hell.
{"user_content": "make it red", "tool_name": "change_background_color", "tool_arguments": "{\"color\": \"red\"}"}
存储芯片在上游扇一扇翅膀，智能手机行业或许面临一场风暴。