Book Review: Perception of Pixelated Images

Despite the fact that we feel immersed in a rich and continuous flow of visual sensations, our visual system samples only a small fraction of the luminance variations present in the environment. Such sparse sampling inevitably comes along with a loss of information. And this is advantageous since it decreases the computational and metabolic needs of the system to e.g., generate, classify, and store images. But sampling must be smartly calibrated so that critical cues are not lost. This seems to be the case for the perception of major visual categories, such as faces and letters, which has been found to rely on a restricted but optimized range of spatial resolutions, also called spatial frequencies (SF; Initial works addressing the SF dependency of human perception manipulated image spatial resolution by means of quantization, also called pixelation. In his recent book, Talis Bachmann reviews how this method contributed to a better understanding of human vision. Quantization consists in dividing an image into equally sized squares, and filling each square with its averaged luminance value (Figures 1A,B). This image process acts like a low-pass SF filter since it maintains the coarse structure of the original picture (i.e., its low SF) but removes its finer details (i.e., its high SF). But quantization also produces a spurious block structure, which adds " alien " high SF to the image. The quantization adventure started with the work published by Harmon and Julesz (1973). The authors quantized the iconic portrait of President Abraham Lincoln and found that portrait recognizability decreased as block size increased (Figures 1A,B). Interestingly, the recognition of the quantized portrait recovered to some extent when block edges were attenuated by low-pass SF filtering (Figure 1C). Harmon and Julesz (1973) interpreted this observation as reflecting " critical band masking, " namely that the high SF of the block structure interfere with (or mask) the low SF carrying portrait information. Such masking was proposed to emerge at primary visual stages of SF extraction, before the integration of visual input into a shape. Later Morrone et al. (1983) challenged the early " critical band masking " interpretation by reporting a seemingly paradoxical finding: portrait recognition improves when high SF random noise is added to the quantized image. If the disruptive effects of quantization on perception were due to inter-SF competition, increasing the power of high SF by adding noise should even more interfere with the recognition …