pil example ssim dynamic quality (snippet)

Python pil example 'ssim dynamic quality'

Functions in program:

def jpeg_dynamic_quality(original_photo):
def _ssim_iteration_count(lo, hi):
def get_ssim_at_quality(photo, quality):

Modules used in program:

import PIL.Image
import cStringIO

python ssim dynamic quality

Python pil example: ssim dynamic quality

import cStringIO
import PIL.Image
from ssim import compute_ssim


def get_ssim_at_quality(photo, quality):
    """Return the ssim for this JPEG image saved at the specified quality"""
    ssim_photo = cStringIO.StringIO()
    # optimize is omitted here as it doesn't affect
    # quality but requires additional memory and cpu
    photo.save(ssim_photo, format="JPEG", quality=quality, progressive=True)
    ssim_photo.seek(0)
    ssim_score = compute_ssim(photo, PIL.Image.open(ssim_photo))
    return ssim_score


def _ssim_iteration_count(lo, hi):
    """Return the depth of the binary search tree for this range"""
    if lo >= hi:
        return 0
    else:
        return int(log(hi - lo, 2)) + 1


def jpeg_dynamic_quality(original_photo):
    """Return an integer representing the quality that this JPEG image should be
    saved at to attain the quality threshold specified for this photo class.

    Args:
        original_photo - a prepared PIL JPEG image (only JPEG is supported)
    """
    ssim_goal = 0.95
    hi = 85
    lo = 80

    # working on a smaller size image doesn't give worse results but is faster
    # changing this value requires updating the calculated thresholds
    photo = original_photo.resize((400, 400))

    if not _should_use_dynamic_quality():
        default_ssim = get_ssim_at_quality(photo, hi)
        return hi, default_ssim

    # 95 is the highest useful value for JPEG. Higher values cause different behavior
    # Used to establish the image's intrinsic ssim without encoder artifacts
    normalized_ssim = get_ssim_at_quality(photo, 95)
    selected_quality = selected_ssim = None

    # loop bisection. ssim function increases monotonically so this will converge
    for i in xrange(_ssim_iteration_count(lo, hi)):
        curr_quality = (lo + hi) // 2
        curr_ssim = get_ssim_at_quality(photo, curr_quality)
        ssim_ratio = curr_ssim / normalized_ssim

        if ssim_ratio >= ssim_goal:
            # continue to check whether a lower quality level also exceeds the goal
            selected_quality = curr_quality
            selected_ssim = curr_ssim
            hi = curr_quality
        else:
            lo = curr_quality

    if selected_quality:
        return selected_quality, selected_ssim
    else:
        default_ssim = get_ssim_at_quality(photo, hi)
        return hi, default_ssim

Python links

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