When light travels through a lens and lands on the digital image sensor, that light will transform into digital signals as pixels. The digital camera processes those pixels in a minimal time difference, but the time distinction still exists; thus, the contrast creates a concept of a pixel as an individual.
How many visual variations will occur when a melting ice ball is placed inside a black box and exposed to a stable light source? The answer should be infinite and unpredictable. If there were a machine that could predict the future and show all the possibilities, then the answer would become concrete. In the meantime, how should the pixel's autonomy be maintained?
《Stochastic Camera》是一個自2018年起的藝術項目系列。《Stochastic Camera (version 0.2) - the melting crystal ball》對機器學習圖像處理的算法邏輯進行了檢視和反思。此項目從紀錄不同冰球在3小時內的融化開始，持續8天，拍攝了約24小時的冰球融化畫面，之後將其轉換為原始像素數據以進行進一步計算。融化的冰球象徵著未來或許不再需要用於預測的水晶球，同時它也作為一個折射物來延伸相機的鏡頭改變折射率。《Stochastic Camera (version 0.2) - the melting crystal ball》利用兩條算式，分別代表整體影像的穩定及個體像素自主。算式的靈感來自集合平均和隨機過程，計算數據的總平均值並預測每個像素的動量，最終形成兩組像素數據和移動圖像。
"Stochastic Camera" is a continuous art project that began in 2018. In version 0.2, re-examines and rethinks machine learning image processing algorithmic logic. The project starts with recording the melting of different ice balls over three hours. It continues for eight days, capturing approximately 24 hours of ice ball melting footage and converting it into raw pixel data for further processing. The melting ice ball symbolises a future world where crystal balls used for divination may no longer be needed while also existing as a refractive object to extend the camera's lens. The "Stochastic Camera (version 0.2)" utilises two independent formulas representing overall stability and individual autonomy. These formulas are inspired by ensemble averaging and stochastic processes, calculating the data's overall mean and predicting each pixel's momentum, which results in two sets of pixel data and moving images.
In a typical image processing workflow, feature extraction is the process that allows machines to identify the most relevant information from raw pixel data. It generates sets of features such as colour, shape and texture. These allow computer scientists to reduce the data dimensions and the number of random variables that need to be considered. Neural networks, such as PixelRNN, also consider the colour data of neighbouring pixels as subsampled image pixels for the target pixel. This results in an efficient, less time-consuming, and less costly way of predicting and generating image emergence. However, from the perspective of a single pixel, an individual living in a two-dimensional image, computer scientists sacrifice the autonomy of individual pixels to reduce computational time and cost.
演算法﹙二﹚：隨機過程 - 個體像素動量此方法先確定了單個像素高於或低於其平均值的概率。與其他用於圖像處理的神經網絡不同，此方法不借用鄰近像素數據作為參考來影響目標像素的變化。相反地，相鄰的像素數據完全不予考慮，賦予每個像素最大程度的自主性來進行演化湧現結果。
《Stochastic Camera (version 0.2) - the melting crystal ball》入選香港藝術中心第28屆ifva獎媒體藝術組決賽。
Algorithm 1: Ensemble Averaging
This method averages the pixel data arrays of the eight ice-ball shots. This formula shows a relationship between the pixels influencing each pixel to form a stable overall image. The final image is similar to superimposing 8 video clips but differs significantly in data handling. This approach means that all strong variations are eliminated by compressing this data into a mean value, and the overall average suppresses each pixel.
Algorithm 2: Individual Pixel Momentum
This method determines the probability of individual pixels being above or below their mean. Unlike other neural networks used for image processing, it does not use neighbouring pixel data as a reference to influence changes in the target pixel. Instead, neighbouring pixel data is not considered, and each pixel is given maximum autonomy to emerge.
"Stochastic Camera (version 0.2) - the melting crystal ball" has been a finalist in the Media Art category of the 28th ifva Awards at the Hong Kong Arts Centre.